{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线性回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算法推导过程中已经给出了求解方法，基于最小二乘法直接求解，但这并不是机器学习的思想，由此引入了梯度下降方法。本次实验课重点讲解其中每一步流程与实验对比分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 主要内容：\n",
    "* 线性回归方程实现\n",
    "* 梯度下降效果\n",
    "* 对比不同梯度下降策略\n",
    "* 建模曲线分析\n",
    "* 过拟合与欠拟合\n",
    "* 正则化的作用\n",
    "* 提前停止策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') #以上工具包之前都有说明，另外这里忽略警告是因为采用不同版本sklearn工具包会提示警告，但对程序运行没影响。\n",
    "np.random.seed(42) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 回归方程：\n",
    "当做是一个巧合就可以了，机器学习中核心的思想是迭代更新"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img4/线性回归/1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "X = 2*np.random.rand(100,1)#np.random.rand产生[0,1)间均匀分布的随机浮点数,返回一个ndarray 100*1维data，2*np.random.rand在[0,2)间，即X范围\n",
    "y = 4+ 3*X +np.random.randn(100,1) # np.random.randn(100,1)是为了在y=4+3*x上加一些随机抖动，这样数据不完全在这条直线上。randn高斯随机\n",
    "X.shape #可以看到X是列向量\n",
    "#这部分是数据的构造"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,y,'b.')#横轴X纵轴y，颜色蓝色，点图（点与点之间不连线，否则plt.plot(X,y,'b')则连线了）\n",
    "plt.xlabel('X_1')\n",
    "plt.ylabel('y')\n",
    "plt.axis([0,2,0,15]) #设置x y轴的取值范围。图像中x从0到2，y从0到15。（默认的话x和y轴的取值范围会设置很大，数据图像就显得很小）\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#下面借助numpy工具箱写一下最小二乘法的代码，这样就不用像第一节课手写所有代码了。\n",
    "X_b = np.c_[np.ones((100,1)),X] #对于y=theta_0 + theta_1*x_1改写成y=theta_0*x_0 + theta_1*x_1,其中x_0=1.\n",
    "#np.c_代表竖着拼接，因为上述X.shape可以看到X是列向量，所以这里再X之前竖着按列再拼接一列1即可。（如果是横着拼接的话，代码为np.r_）\n",
    "theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y) #这句话是（X^T*X）^{-1}*X^T*y,即最小二乘法求theta，见上述图片公式。\n",
    "# np.linalg.inv代表矩阵求逆，.T代表矩阵转置，.dot()代表矩阵乘法（也是numpy的方法）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.21509616],\n",
       "       [2.77011339]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_best #显示最小二乘法得到的theta参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.21509616],\n",
       "       [9.75532293]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new = np.array([[0],[2]]) #创建一个返回值是ndarray的数组。这里有两个点0和2构成\n",
    "X_new_b = np.c_[np.ones((2,1)),X_new] #上述LSE（最小二乘法）中用的表达方式这里要一致，所以也要加一列1\n",
    "y_predict = X_new_b.dot(theta_best) #用训练好的theta计算样本是0和2时的预测值。\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_new,y_predict,'r--') #因为样本0和2分别是X的最小点和最大点，所以这个线性方程的直线可以直接连接这两个点取得。颜色为红，线条虚线\n",
    "plt.plot(X,y,'b.')\n",
    "plt.axis([0,2,0,15])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sklearn api文档：\n",
    "不用背，用到的时候现查完全够用的。\n",
    "https://scikit-learn.org/stable/modules/classes.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.77011339]]\n",
      "[4.21509616]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression \n",
    "#见： https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression\n",
    "# 上述网页有使用的examples说明可以直接复制粘贴。\n",
    "lin_reg = LinearRegression() #实例化，得到对象。 上述LSE在sklearn下直接引用。\n",
    "lin_reg.fit(X,y) #训练用网页中的Methods的fit方法\n",
    "print (lin_reg.coef_)# 网页中的属性Attributes中有可以调用的属性，coef_ 代表参数theta，也叫权重参数\n",
    "print (lin_reg.intercept_) # 网页中的属性Attributes中有可以调用的属性，intercept_ 代表截断，也叫偏置参数\n",
    "#通过输出可以看到有上述我们用numpy下代码得到的结果一致。这样我们就可以用sklearn工具包实现LSE了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 梯度下降\n",
    "核心解决方案，不光在线性回归中能用上，还有其他算法中能用上，比如神经网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img4/线性回归/2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 问题：步长太小"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img4/线性回归/3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 问题：步长太大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img4/线性回归/4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img4/线性回归/5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "学习率应当尽可能小，随着迭代的进行应当越来越小。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 标准化的作用:\n",
    "- 拿到数据之后的标准化操作，sklearn下的预处理模块下就包含。\n",
    "- sklearn下的预处理模块见：https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing\n",
    "- 另外，从有监督学习、无监督学习及模型评估等角度sklearn有说明书，可以参考：https://scikit-learn.org/stable/user_guide.html#user-guide\n",
    "- 是否需要标准化的介绍：https://blog.csdn.net/shwan_ma/article/details/80154888 并不是所有的情况都需要的。建议：决定数据和方法后搜下需要与否。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img4/线性回归/6.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 批量梯度下降计算公式\n",
    "- 由分量theta_j到矩阵化的写法，由此可以由下述矩阵化的公式书写代码。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img4/线性回归/7.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 批量梯度下降"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "eta = 0.1 #学习率\n",
    "n_iterations = 1000 #迭代次数\n",
    "m = 100 #样本个数\n",
    "theta = np.random.randn(2,1) #参数的初始点的选择是随机生成的。\n",
    "for iteration in range(n_iterations): #迭代100步\n",
    "    gradients = 2/m* X_b.T.dot(X_b.dot(theta)-y) #按照上述右侧的公式做矩阵乘法。\n",
    "    theta = theta - eta*gradients #迭代方向是负梯度，所以是减法。由于不是矩阵乘法，所以不用.dot，用*即可。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.21509616],\n",
       "       [2.77011339]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta #可以看到与之前的LSE是一致的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.21509616],\n",
       "       [9.75532293]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new_b.dot(theta) #做预测：还是用之前定义的X_new，但需转化为X_new_b，这样乘以训练好的theta就可以得到预测值。即X为0及X为2时的预测值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#为了观察不同学习率下的实验结果，这里首先定义在以学习率为参数的方法\n",
    "theta_path_bgd = [] #定义个空列表，用于存储迭代过程中的各个theta值\n",
    "# 注意：None表示空，但它不等于空字符串、空列表，也不等同于False。即[]是空列表，但它不等于None。None类型只有None一个值。\n",
    "\n",
    "def plot_gradient_descent(theta,eta,theta_path = None): # 默认theta_path = None用于判断当前theta是否用于保存，当非None时保存迭代的theta\n",
    "    m = len(X_b) #计算样本个数\n",
    "    plt.plot(X,y,'b.') #绘制原始数据\n",
    "    n_iterations = 1000 #设置迭代次数\n",
    "    for iteration in range(n_iterations):\n",
    "        y_predict = X_new_b.dot(theta) #以样本X起点0和终点2预测对应的两个y值，连接这两点，就可以绘制当前theta下的回归直线\n",
    "        plt.plot(X_new,y_predict,'b-') #绘制当前theta下的回归直线\n",
    "        gradients = 2/m* X_b.T.dot(X_b.dot(theta)-y)\n",
    "        theta = theta - eta*gradients #用梯度下降方法更新theta\n",
    "        if theta_path is not None: #当theta_path不为None时，保存theta，把这步迭代后的theta放入theta_path的尾部\n",
    "            theta_path.append(theta)\n",
    "    plt.xlabel('X_1')\n",
    "    plt.axis([0,2,0,15])\n",
    "    plt.title('eta = {}'.format(eta))#因为对比不同的学习率，所以用学习率来命名。.format()意义在于以某一格式输出字符串(这里用默认格式)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta = np.random.randn(2,1) #初始化theta，列向量\n",
    "#公平起见：下面不同学习率下初始的theta用的都是用的同一个theta值（即上行代码随机产生后的theta值）\n",
    "\n",
    "#观察不同学习率下的实验结果\n",
    "plt.figure(figsize=(10,4))  #表示figure的大小为宽、长（单位为inch）\n",
    "plt.subplot(131) #子图subplot 131：一行三列的第一个\n",
    "plot_gradient_descent(theta,eta = 0.02)\n",
    "plt.subplot(132)#子图subplot 132：一行三列的第二个\n",
    "plot_gradient_descent(theta,eta = 0.1,theta_path=theta_path_bgd) #这行代码中theta_path是非None，开始对变量theta_path赋值。见上述def方法\n",
    "plt.subplot(133)#子图subplot 133：一行三列的第三个\n",
    "plot_gradient_descent(theta,eta = 0.5)\n",
    "plt.show()\n",
    "\n",
    "#蓝色线的变化过程反应了theta的迭代过程，另外可以看到学习率较小，每次变化的较小。画出1000条直线，但由于几步之后就稳定了，\n",
    "#所以都重叠在一起了，这里每个子图都有1000条直线。学习率过大时，如eta=0.5，则产生不太好的效果，学习的效果不太好。\n",
    "#所以学习率小一点顶多是慢一点，但学习率过大则有做不好的风险。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机梯度下降"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img4/线性回归/8.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta_path_sgd=[] #刚刚用theta_path_bgd保存了一个批量梯度下降法的在eta = 0.1时的theta变化过程，这里用theta_path_sgd保存随机梯度下降法的。\n",
    "m = len(X_b)\n",
    "np.random.seed(42)\n",
    "n_epochs = 50\n",
    "# epoch可以翻译成\"期\"。 比如一共100个样本，每个样本依次用来训练这个神经网络，当这100个样本都被用过一遍之后，我们就说完成了一期训练。\n",
    "# 如果设置epoch=5，意思就是说把这个神经网络进行了五期训练。一个epoch就是把整个训练集过一遍。如果是用sgd的话（每次随机选取样本），每\n",
    "# 训练100个随机样本就是一个epoch。\n",
    "\n",
    "#定义学习率的衰减过程：随着迭代步数的增加，loss离最小值点越近时，学习率越小（即步长越小）。\n",
    "t0 = 5 #t0和t1是人为指定，没有一个固定的标准，适合即可。\n",
    "t1 = 50\n",
    "\n",
    "def learning_schedule(t):  #学习率的衰减过程，动态调整，学习率逐渐变小。\n",
    "    return t0/(t1+t)  #迭代次数t越大，则分母越大，所以学习率越来越小\n",
    "\n",
    "theta = np.random.randn(2,1) #初始化theta的选取\n",
    "\n",
    "for epoch in range(n_epochs): #由上述“期”概念知，这里sgd共训练50期，每1期100个随机样本。所以两层循环：一层“期”，一层“随机样本”\n",
    "    for i in range(m):\n",
    "        if epoch < 10 and i<10: #都展示的话，图像较乱，这里只展示迭代的前几步。但要是epoch选太小，则没迭代好，效果可能不好，综上选10.\n",
    "            y_predict = X_new_b.dot(theta)\n",
    "            plt.plot(X_new,y_predict,'r-') \n",
    "        random_index = np.random.randint(m)#选择随机的一个样本 .randint返回一个随机整型数，范围从低（包括）到高（不包括），即[low, high)。如果没有写参数high的值，则返回[0,low)的值。\n",
    "        xi = X_b[random_index:random_index+1] #[n]为取list中第n+1个元素,[m:n]为取list中 第m+1 个元素到 第n+1 个元素组成的list，其中包含第m+1个元素，不包含第n+1个元素。(从数学上讲，左边是闭区间，右边是开区间);\n",
    "        # 所以这里其实是X_b的第random_index+1个值，只是一个值而不是两个。但格式是ndarray，这样下面有关xi的乘法仍然用的是矩阵乘法.dot而不是*\n",
    "        yi = y[random_index:random_index+1]\n",
    "        gradients = 2* xi.T.dot(xi.dot(theta)-yi) #随机梯度下降法的迭代公式\n",
    "        eta = learning_schedule(epoch*m+i)#学习率的衰减过程，一期有m个，所以epoch*m，每一期内部，随机样本的选取迭代i，所以当前是epoch*m+i次迭代，对应迭代次数得到衰减后的学习率。\n",
    "        theta = theta-eta*gradients\n",
    "        theta_path_sgd.append(theta)\n",
    "        \n",
    "plt.plot(X,y,'b.')\n",
    "plt.axis([0,2,0,15])   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MiniBatch梯度下降"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_path_mgd=[] \n",
    "n_epochs = 50\n",
    "minibatch = 16 #人为设定minibatch的值\n",
    "theta = np.random.randn(2,1)\n",
    "t0, t1 = 200, 1000\n",
    "def learning_schedule(t): #学习率的衰减\n",
    "    return t0 / (t + t1)\n",
    "np.random.seed(42) #如果不指定随机种子，则下面每次运行的theta是不一样的，这是没有问题的，体现了随机的思想。当指定种子时则不具有下面洗牌的思想。不一定选42，指定一个数即可。\n",
    "t = 0\n",
    "for epoch in range(n_epochs):\n",
    "    shuffled_indices = np.random.permutation(m) #随机排列一个序列，或者数组；这样每1期epoach下各样本顺序不同，由此不同期同一个次序的minibatch内样本不同。\n",
    "    #如果这里不打乱，则下述“for i in range(0,m,minibatch):”每一期的同一个minibatch的样本都一样。\n",
    "    X_b_shuffled = X_b[shuffled_indices]\n",
    "    y_shuffled = y[shuffled_indices]\n",
    "    for i in range(0,m,minibatch):\n",
    "        t+=1\n",
    "        xi = X_b_shuffled[i:i+minibatch]\n",
    "        yi = y_shuffled[i:i+minibatch]\n",
    "        gradients = 2/minibatch* xi.T.dot(xi.dot(theta)-yi)\n",
    "        eta = learning_schedule(t)\n",
    "        theta = theta-eta*gradients\n",
    "        theta_path_mgd.append(theta) #存储theta值的迭代过程\n",
    "\n",
    "#print(type(theta_path_mgd)) #可以看到theta_path_mgd是list，即列表格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.25490685],\n",
       "       [2.80388784]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "### 3种策略的对比实验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_path_bgd = np.array(theta_path_bgd) #把列表格式的结果都转化为ndarray格式的结果\n",
    "theta_path_sgd = np.array(theta_path_sgd)\n",
    "theta_path_mgd = np.array(theta_path_mgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))#指定图片大小\n",
    "plt.plot(theta_path_sgd[:,0],theta_path_sgd[:,1],'r-s',linewidth=1,label='SGD')#随机梯度下降 红色's'代表的符号，线宽1\n",
    "plt.plot(theta_path_mgd[:,0],theta_path_mgd[:,1],'g-+',linewidth=2,label='MINIGD')#Minibatch梯度下降 绿色'+'代表的符号，线宽2\n",
    "plt.plot(theta_path_bgd[:,0],theta_path_bgd[:,1],'b-o',linewidth=3,label='BGD')#批量梯度下降 蓝色'o'代表的符号，线宽3\n",
    "plt.legend(loc='upper left')\n",
    "plt.axis([3.5,4.5,2.0,4.0]) #为了图形的辨别性，限定x和y的显示范围\n",
    "plt.show()\n",
    "\n",
    "#可以看到BGD是直接到收敛点去的，但对所有样本要计算，对规模大的问题会耗时。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实际当中用minibatch比较多，一般情况下选择batch数量应当越大越好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "### 多项式回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = 100\n",
    "X = 6*np.random.rand(m,1) - 3\n",
    "#np.random.rand产生[0,1)间均匀分布的随机浮点数,返回一个ndarray 100*1维data，6*np.random.rand在[0,6)间，再-3即X范围为[-3,3)\n",
    "y = 0.5*X**2+X+np.random.randn(m,1)# np.random.randn(100,1)是为了在y=0.5*X**2+X上加一些随机抖动，这样数据不完全在这条曲线上。randn高斯随机"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,y,'b.')\n",
    "plt.xlabel('X_1')\n",
    "plt.ylabel('y')\n",
    "plt.axis([-3,3,-5,10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.38942838])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "#见：https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures\n",
    "# For example, if an input sample is two dimensional and of the form [a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2].\n",
    "poly_features = PolynomialFeatures(degree = 2,include_bias = False) #实例化，include_bias偏置项不加入，即include_bias = False\n",
    "#If include_bias =  True (default), then include a bias column, the feature in which all polynomial powers are zero (i.e. a column of ones - acts as an intercept term in a linear model)\n",
    "#因为后面引入sklearn.linear_model的LinearRegression方法，只需要引入数据即可，不需要手动再加偏置项，所以这里include_bias = False\n",
    "#这样，由于我们是一维特征，即x1，则degree = 2,include_bias = False意味着通过变换我们可以得到[x1,x1^2].若是令include_bias = True，则得到[1,x1,x1^2]\n",
    "X_poly = poly_features.fit_transform(X)\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures.fit_transform\n",
    "#fit_transform包含两步操作，1.fit：由x1计算x1,x1^2; 2.transform：把计算好的x1，x1^2转化成[x1,x1^2]的ndarray并做返回。\n",
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.38942838, 5.709368  ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_poly[0] #即X转化为array([X, X^2 ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.709367983149424"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.38942838 ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.95038538 0.52577032]]\n",
      "[-0.0264767]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression #和上面的代码一样，只是输入的X转化为array([X, X^2 ])\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_poly,y)\n",
    "print (lin_reg.coef_) #权重项，[theta_1,theta_2]\n",
    "print (lin_reg.intercept_) #偏置项 theta_0\n",
    "#即得到y = theta_1*x + theta_2*x^2 + theta_0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_new = np.linspace(-3,3,100).reshape(100,1) #linspace(-3,3,100)定义-3到3的等距100个点，得行向量的ndarray；reshape(100,1)转化为100*1的列向量的ndarray格式\n",
    "X_new_poly = poly_features.transform(X_new)#与上述过程相同，但之前fit_transform过了，这其中包含了fit过程。\n",
    "# 即这个方法的书写逻辑使得该实例化后的对象保存了fit过程，再在该对象转化数据时只需要transform()即可，不用重新fit，不再用fit_transform()了。\n",
    "y_new = lin_reg.predict(X_new_poly) #训练参数用的就是转化后的数据，所以在predict中也要输入转化后的数据，即不能用X_new要用X_new_poly。\n",
    "plt.plot(X,y,'b.')\n",
    "plt.plot(X_new,y_new,'r--',label='prediction')\n",
    "plt.axis([-3,3,-5,10])\n",
    "plt.legend()\n",
    "plt.show()#可以看到用线性回归做的结果不一定是线性的，可以通过改变特征为非线性特征来达到非线性回归的目的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#不同degree下的对比实验--多项式回归\n",
    "from sklearn.pipeline import Pipeline #https://scikit-learn.org/stable/modules/classes.html#module-sklearn.pipeline，即管线,含义“流水线”\n",
    "# Pipeline即给定一种流水线流程的定义\n",
    "from sklearn.preprocessing import StandardScaler #标准化的方法，用的是z = (x - mean) / sigma\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler\n",
    "plt.figure(figsize=(12,6))\n",
    "for style,width,degree in (('g-',1,100),('b--',1,2),('r-+',1,1)): #三组实验，参数是(颜色+线条，线宽，degree取值)\n",
    "    poly_features = PolynomialFeatures(degree = degree,include_bias = False) #多项式特征转化\n",
    "    std = StandardScaler() #标准化\n",
    "    lin_reg = LinearRegression() #建模\n",
    "    polynomial_reg = Pipeline([('poly_features',poly_features),\n",
    "             ('StandardScaler',std),\n",
    "             ('lin_reg',lin_reg)]) #Pipeline内部参数分别是[('自己定义流程1的名字'，流程1),('自己定义流程2的名字'，流程2)...]\n",
    "    polynomial_reg.fit(X,y) #训练模型，传入原始数据样本X和标签y，按照上述规定的流程模块进行。\n",
    "    y_new_2 = polynomial_reg.predict(X_new) #用数据X_new做预测（用于后续画图），得到的预测结果记为y_new_2\n",
    "    plt.plot(X_new,y_new_2,style,label = 'degree   '+str(degree),linewidth = width) #style,width,degree在for循环的定义中都有定义\n",
    "plt.plot(X,y,'b.') #原始数据绘图\n",
    "plt.axis([-3,3,-5,10])\n",
    "plt.legend()\n",
    "plt.show() #展示三个不同degree的多项式回归过程，100degree显然是过拟合了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征变换的越复杂，得到的结果过拟合风险越高，不建议做的特别复杂。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据样本数量对结果的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error #评估函数\n",
    "# 见：https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error\n",
    "from sklearn.model_selection import train_test_split #功能：对数据集进行分割，分成训练集和测试集的方法（我们是把训练集分为训练集和验证集）\n",
    "# 见：https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split\n",
    "def plot_learning_curves(model,X,y): #定义学习曲线，查看不同样本量下的学习曲线\n",
    "    X_train, X_val, y_train, y_val = train_test_split(X,y,test_size = 0.2,random_state=100)#训练集占80%，测试集占20%，随机种子random_state指定一个数即可，使得每次拿的具体部分都一样\n",
    "    #返回值是训练集X，测试集X，训练集标签Y，测试集标签Y；我们是把训练集分为训练集和验证集，所以是训练集X，验证集X，训练集标签Y，验证集标签Y\n",
    "    train_errors,val_errors = [],[]#定义训练集和验证集的误差，list结构\n",
    "    for m in range(1,len(X_train)): #从一个样本开始训练模型，到用len(X_train)个样本训练模型。即检查不同训练样本个数对验证集下预测误差的影响。\n",
    "        model.fit(X_train[:m],y_train[:m]) #训练\n",
    "        y_train_predict = model.predict(X_train[:m])#训练集的预测结果\n",
    "        y_val_predict = model.predict(X_val)#验证集的预测结果\n",
    "        train_errors.append(mean_squared_error(y_train[:m],y_train_predict[:m])) #训练集的误差，评估标准MSE\n",
    "        val_errors.append(mean_squared_error(y_val,y_val_predict)) #验证集的误差，评估标准MSE\n",
    "    plt.plot(np.sqrt(train_errors),'r-+',linewidth = 2,label = 'train_error') #绘图时用的是RMSE，即sqrt（MSE）\n",
    "    plt.plot(np.sqrt(val_errors),'b-',linewidth = 3,label = 'val_error') #绘图时用的是RMSE，即sqrt（MSE）\n",
    "    plt.xlabel('Trainsing set size')\n",
    "    plt.ylabel('RMSE')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lin_reg = LinearRegression()\n",
    "plot_learning_curves(lin_reg,X,y)\n",
    "plt.axis([0,80,0,3.3])\n",
    "plt.show()#训练样本越少，过拟合风险越大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据量越少，训练集的效果会越好，但是实际测试效果很一般。实际做模型的时候需要参考测试集和验证集的效果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多项式回归的过拟合风险"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "polynomial_reg = Pipeline([('poly_features',PolynomialFeatures(degree = 25,include_bias = False)),\n",
    "             ('lin_reg',LinearRegression())])\n",
    "plot_learning_curves(polynomial_reg,X,y)\n",
    "plt.axis([0,80,0,5])\n",
    "plt.show()#训练样本越少，过拟合风险越大；且与上述线性回归相比，train与validation的error差距更大了，即model越复杂越易过拟合。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "越复杂越过拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正则化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对权重参数进行惩罚，让权重参数尽可能平滑一些，有两种不同的方法来进行正则化惩罚:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./img4/线性回归/9.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7ucaclpYGADhw4EChjJ+8HjwHDx5UlZnVq1cPANCgQQPExcWpbZ/3fXGZYLq6unB3d8e9e/fKNVYioprKRN8E/mP8cT/pPmyMNF96BTCoQ0RERFTtZDIZHB0dcf78+VLvk5qaCkNDQ8jy5rWvBhUpjXq2HKuyJSQk4M6dO/jll19UgZCTJ0+Wat+zZ8+iUaNGAIDExETcvXsXLVq0KPW5T506hdWrV6uCdQ8ePEB8fHyJ+1R1+VXLli2hr6+PyMjIYkutnJwKz9Li4eGBTz/9FDk5Oapg1pEjR9CsWTNYWFgUeRy5XI5r164VClYSEdV2BjoG8Hb11vQw1DCoQ0RERFQNnj59ioyMDCQnJ+PPP//EoUOH4OvrW6p9e/fujbS0NOjp6aF///744Ycf4ObmVsUj1m4WFhawsrLCunXrYGdnh8jISHz88cel2nf+/PmwsrKCra0tPv30U1hbW2Po0KGlPrebmxu2bt2KTp06ISUlBXPmzFFlvhSnouVX2dnZuHnzpur5w4cPcfnyZRgbG6NJkyYwMTHB7NmzMXPmTCgUCnTr1g3Jyck4deoUTE1NMW7cuCKPO3r0aHz11VeYOHEiPvroI1y/fh3Lly/HsmXLVNvMnz8fXbt2RZMmTZCUlITFixfj/v37eOutt8r9eoiIqHIwqENERERUDT744AOsXbsWACCVSvHqq69i1apVJe5jaGiI8ePHo3fv3jA1NcXFixexdOlSeHp6Ijg4GI6OhXvZ5MnKykJWVpbq+5SU8veu0UZSqRQ7d+7E9OnT0bp1azRr1gwrVqxAr169nrvvokWLMGPGDISEhKB9+/bYv3+/2uxPz7NhwwZMnjwZHTp0gKOjI7755hu12bGqQnR0tKo3DgAsWbIES5YsQc+ePREYGAgAWLBgAWxsbPDtt98iLCwM5ubm6NChAz755JNij2tmZoaAgABMnToVHTt2hLW1Nb744gu1ssDExERMmjRJ1Ti5Y8eOOH36NFq2bFllr5eIiEpHoixYIFxDpKSkwMzMDMnJyTA1LX8dNhEREVUtvmfnu337NqKiohAdHY3ffvsNenp6+Pnnn1UzDZXWyZMn0aNHD0yePBlr1qwpdrsvv/wSX331VaHlRf0sMjMzER4ejsaNG8PAwKBM46lJAgMD0bt3byQmJlZLo+m6rK78ThFR3XIs7BikEimczZ3hbO5cZbNfleXzk7RKRkBEREREapo3b45+/frhjTfewF9//YW0tDQMHjwYZb2/1q1bN3Tp0gVHjx4tcbu5c+ciOTlZ9Xjw4EFFhk9ERFTnzfSfiT5b+qDZqmZQQjvyYxjUISIiItKAESNG4Pz587h7926Z93V0dMSTJ09K3EZfXx+mpqZqDyIiIiofpVKJ+8n3AQCNzBpBKtGOcAp76hARERFpQEZGBgBRDlVWYWFhsLHRjqlUa5JevXqVOTOKiIgIAJIyk5CSJfrTOZkXnk1QU7QjtERERERUSz169KjQspycHGzZsgX16tVTNZuNiYnB7du3kZOTo9ru8ePHhfY9ePAgLl68iJdeeqnqBk1ERERq8rJ0AMDZzFlzA3kGM3WIiIiIqtCUKVOQkpKCHj16oGHDhoiNjcX27dtx+/Zt/PDDDzA2NgYgeuBs3rwZ4eHhcHZ2BgB4enrC3d0dnTp1gpmZGYKDg7Fx40Y4OjqWOKMRERERVa77SflBnRqbqXP+/HlMmzYNrVq1gpGRERo1agQfH59S1YL7+flBIpEU+YiNjS33CyAiIiLSZr6+vpBKpfj555/xzjvvYOnSpXBwcMC+ffswa9as5+4bEhKCb775Bu+99x4OHz6MSZMm4fz582WeNYuIiIjKLyIpQvXc2dxZY+N4Vpkydb777jucOnUKI0eORNu2bREbG4tVq1ahQ4cOOHv2LFq3bv3cY8yfPx+NGzdWW8YpJWufqCggJARwcwMcHDQ9GiIiIs0ZNWoURo0a9dzt/Pz84Ofnp7Zs4cKFWLhwYRWNjIiIiEqrYPmVk5n2ZOqUKagza9Ys7NixA3p6eqplvr6+aNOmDRYtWoRt27Y99xgDBgxAp06dyj5SqjE2bAAmTwYUCkAqBdatAyZO1PSoiIiIiIiIiMqnYKZOjS2/8vT0VAvoAICbmxtatWqFW7dulfo4qampkMvlZTk11RBRUfkBHUB8nTJFLCciIiIiIiKqifIydWQSGRxMtaccpcKzXymVSsTFxcHa2rpU2/fu3RumpqYwNDTEkCFDEBISUtEhkBYJCckP6OSRy4F79zQzHiIiIqq7IiIiIJFIcPny5VLv4+fnx9YARERUiI5UB7pSXTQ0bQgdqfbMOVXhoM727dvx8OFD+Pr6lridoaEhxo8fj59++gl79+7Fhx9+iGPHjsHT0xMPHjwocd+srCykpKSoPUg7ubmJkquCZDKgSRPNjIeIiIioNrhx4waGDx8OZ2dnSCQS/Pjjj5oeEhFRnRL0VhAyP8vExckXNT0UNRUK6ty+fRtTp06Fh4cHxo0bV+K2Pj4+2LRpE9544w0MHToUCxYsgL+/PxISEvD111+XuO+3334LMzMz1cPR0bEiw6Yq5OAgeujIZOJ7mQxYu5bNkomIiIgqIj09HS4uLli0aBEaNGig6eEQEdVJUokU1oalq1KqLuUO6sTGxuLll1+GmZkZdu/eDVneVXwZdOvWDV26dMHRo0dL3G7u3LlITk5WPZ6X2UOaNXEiEBEB/POP+MomyURERFQVDh8+jG7dusHc3BxWVlYYNGgQQkNDi90+MDAQEokEBw4cQNu2bWFgYICuXbvi+vXrhbb19/dHixYtYGxsjJdeegkxMTGqdefPn4eXlxesra1hZmaGnj17Ijg4uEpeY57OnTtj8eLFGDVqFPT19av0XEREVHOUK6iTnJyMAQMGICkpCYcPH4a9vX25B+Do6IgnT56UuI2+vj5MTU3VHqTdHByAXr2YoUNERERV5+nTp5g1axYuXLiAY8eOQSqVYtiwYVA82+DvGXPmzMEPP/yA8+fPw8bGBoMHD0ZOTo5qfXp6OpYsWYKtW7fi+PHjiIyMxOzZs1XrU1NTMW7cOJw8eRJnz56Fm5sbBg4ciNTU1GLPuX37dhgbG5f4OHHiRMX/UYiIqE4pc3efzMxMDB48GHfv3sXRo0fRsmXLCg0gLCwMNjY2FToGEREREVWBpUvFAwC2bRN3bPKEhwPdu4vnw4YBK1eq7ztkCJCXvfLsNJh+fsBnn4nnK1YAr75aruENHz5c7fuNGzfCxsYGN2/eROvWrYvdb968efDy8gIAbN68GQ4ODti7dy98fHwAADk5OVizZg1cXV0BANOmTcP8+fNV+/fp00fteOvWrYO5uTn+/fdfDBo0qMhzDhkyBF26dCnx9TRs2LDE9UREpBmLTi7ChegLaGLZBB94fAAbI+2JYZQpqCOXy+Hr64szZ85g37598PDwKHK7mJgYJCcnw9XVFbq6ugCAx48fFwreHDx4EBcvXsT06dPLOXwiIiIiqjIpKcDDh+J5Vpb6Ork8f11iYuF9Hz/OX/+sp0/z16Wnl3t4ISEh+OKLLxAUFIT4+HhVhk5kZGSJQZ2Cn2EtLS3RrFkz3Lp1S7XM0NBQFdABADs7Ozx69Ej1fVxcHD777DMEBgbi0aNHkMvlSE9PR2RkZLHnNDExgYmJSbleJxERadY/Ef8gIDQAAPCBxwcaHo26MgV1PvjgA/z5558YPHgwnjx5gm3btqmtHzNmDADRA2fz5s0IDw+Hs7MzAMDT0xPu7u7o1KkTzMzMEBwcjI0bN8LR0RGffPJJ5bwaIiIiIqo8pqZAXvbIs31cZLL8dRYWhfe1sclf/ywjo/x1hoblHt7gwYPh5OSEX375Bfb29lAoFGjdujWys7PLfUwAqpuSeSQSCZRKper7cePGISEhAcuXL4eTkxP09fXh4eFR4nm3b9+OKVOmlHjeQ4cOoXte9hMREWmNe0/uAQBM9U21rlFymYI6ly9fBgDs378f+/fvL7Q+L6hTFF9fXxw4cAABAQFIT0+HnZ0dJk2ahHnz5sHW1rZsoyYiIiKiqjdrlngUpXHjwmVVBf35Z/Hrxo8XjwpISEjAnTt38Msvv6gCISdPnizVvmfPnkWjRo0AAImJibh79y5atGhR6nOfOnUKq1evxsCBAwEADx48QHx8fIn7sPyKiKhmypZnIyIpAgDQxLIJJBKJZgf0jDIFdQIDA0u1nZ+fH/z8/NSWLVy4EAsXLizL6YiIiIiIimRhYQErKyusW7cOdnZ2iIyMxMcff1yqfefPnw8rKyvY2tri008/hbW1NYYOHVrqc7u5uWHr1q3o1KkTUlJSMGfOHNSrV6/EfSpafpWdnY2bN2+qnj98+BCXL1+GsbExmjRpUu7jEhFRySKSIqBQivLeJpba9/e23FOaExERERFpilQqxc6dO3Hx4kW0bt0aM2fOxOLFi0u176JFizBjxgx07NgRsbGx2L9/P/T09Ep97g0bNiAxMREdOnTA2LFjMX36dNSvX7+8L6VUoqOj4e7uDnd3d8TExGDJkiVwd3fHW2+9VaXnJSKq6/JKrwCgiYX2BXXKPPsVEREREZE26Nevnyp7JU/B3jfOzs5q3+fp1q0brl+/XuQxx48fj/HPlIYNHTpU7Tju7u44f/682jYjRowo6/DLpLjXQkREVUstqMNMHSIiIiIiIiKimoFBHSIiIiIiIiKiGkjbgzosvyIiIiKiOqFXr14sYSIiojLJC+oY6hqigXEDDY+mMAZ1iIiIiIiIiIiKMLPrTNyOv41cRa7WTWcOMKhDRERERP9hFgtVFv4uEVFt8U7ndzQ9hBKxpw4RERFRHaerqwsASE9P1/BIqLbI+13K+90iIqKqwUwdIiIiojpOJpPB3Nwcjx49AgAYGhpqZYo5aT+lUon09HQ8evQI5ubmkMlkmh4SEVGtxqAOEREREaFBA9H8MS+wQ1QR5ubmqt8pIqKa6n7SfdTTrQcbQxutvdnBoA4RERERQSKRwM7ODvXr10dOTo6mh0M1mK6uLjN0iKhWeO/Qe9h/dz9M9U1xd9pd2BrbanpIhTCoQ0REREQqMpmMF+RERETIn848W54NGyMbDY+maGyUTERERERERERUgFwhR2hiKADA1cIVUol2hk+0c1RERERERERERBoSlRKFbHk2AKCJZRMNj6Z4DOoQERERERERERWQV3oFAG6WbhocSckY1CEiIiIiIiIiKqBgUIeZOkRERERERERENQSDOkRERERERERENdC9RAZ1iIiIiIiIiIhqnLxMHT2ZHhxMHTQ8muIxqENERERERERE9B+lUonwxHAAgIuFC2RSmYZHVDwdTQ+AiIiIiIiIiEhbSCQSJHyYgPCkcKRkpWh6OCViUIeIiIiIiIiIqAB9HX00t26u6WE8F8uviIiIiIiIiIhqIAZ1iIiIiIiIiIhqIJZfERERERERERH9Z/nZ5UjMTEQTyybwaeUDPZmepodULAZ1iIiIiIiIiIj+s+HSBlx7dA26Ul2Maj1K08MpEcuviIiIiIiIiIgA5CpycSfhDgDAzcoNOlLtzoVhUIeIiIiIiIiICEBYYhiy5dkAgJY2LTU8mudjUIeIiIioCt24cQMjR46Ei4sLDA0NYW1tjR49emD//v2l2j8pKQmTJ0+GjY0NjIyM0Lt3bwQHB1fxqImIiOqmm49vqp63tNb+oI525xERERER1XD3799Hamoqxo0bB3t7e6Snp+P333/HkCFDsHbtWkyePLnYfRUKBV5++WVcuXIFc+bMgbW1NVavXo1evXrh4sWLcHNzq8ZXQkREVPupBXWKy9RJTwcMDatpRCWTKJVKpaYHUVYpKSkwMzNDcnIyTE1NNT0cIiIiKgbfs4sml8vRsWNHZGZm4vbt28Vu99tvv8HX1xe7du3CiBEjAACPHz9G06ZNMWDAAOzYsaPU5+TPgoiI6PnG7h2LbVe3AQCuvH0FbW3bqm+gVALOzoCtLTB8OPDRR5U+hrK8Z7P8ioiIiKiayWQyODo6IikpqcTtdu/eDVtbW7z66quqZTY2NvDx8cG+ffuQlZVVxSMlIiKqW/IydaQSKZpaNS28wdWrQGQkcP48EBhYvYMrAoM6RERERNXg6dOniI+PR2hoKJYtW4ZDhw6hb9++Je5z6dIldOjQAVKp+ke2F154Aenp6bh7926x+2ZlZSElJUXtQURERMVTKBW49fgWAMDVwhUGOgaFN0pMBNq1E88HDarG0RWNQR0iIiKiavDBBx/AxsYGTZo0wezZszFs2DCsWrWqxH1iYmJgZ2dXaHnesujo6GL3/fbbb2FmZqZ6ODo6VuwFEBER1XL3k+4jIzcDQAn9dHr1Ai5fBu7fB0aPrraxFYdBHSIiIqJq8P777+PIkSPYvHkzBgwYALlcjuzs7BL3ycjIgL6+fqHlBgYGqvXFmTt3LpKTk1WPBw8eVOwFEBER1XJSiRTvdnoXvZx7oatD15I3btQIsLConoGVgLNfEREREVWD5s2bo3nz5gCAN954A97e3hg8eDCCgoIgkUiK3KdevXpF9s3JzMxUrS+Ovr5+kQEhIiIiKpqTuRN+evknTQ+jTJipQ0RERKQBI0aMwPnz50vsi2NnZ4eYmJhCy/OW2dvbV9n4iIiI6Bm5uZoeQSEM6hARERFpQF7pVHJycrHbtG/fHsHBwVAoFGrLg4KCYGhoiKZNi5iVg4iIiCpfdjbg4CCaI2/bpunRqDCoQ88VFQX884/4SkRERGXz6NGjQstycnKwZcsW1KtXDy1bikaMMTExuH37NnJyclTbjRgxAnFxcdizZ49qWXx8PHbt2oXBgwezvIqIiKiSKJVKxKfHF7/ByZNAXBxw4ABw+HD1Dew52FOHSrRhAzB5MqBQAFIpsG4dMHGipkdFRERUc0yZMgUpKSno0aMHGjZsiNjYWGzfvh23b9/GDz/8AGNjYwCisfHmzZsRHh4OZ2dnACKo07VrV0yYMAE3b96EtbU1Vq9eDblcjq+++kqDr4qIiKh2iU6NhsMyB9gY2mBC+wn4zus79Q0SEgB7eyA6WiumMs/DoA4VKyoqP6ADiK9TpgD9+4usMyIiIno+X19fbNiwAT///DMSEhJgYmKCjh074rvvvsOQIUNK3Fcmk+HgwYOYM2cOVqxYgYyMDHTu3Bl+fn5o1qxZNb0CIiKi2u/m45sAgMfpjyFXygtvMHIkMGKEmM7c1bV6B1cCBnWoWCEh+QGdPHI5cO8egzq1WVSU+Nm7ufHnTERUGUaNGoVRo0Y9dzs/Pz/4+fkVWm5hYYH169dj/fr1VTA6IiIiAoBb8bdUz1tYtyh6I4kEcHevphGVDnvqULHc3ETJVUEyGdCkiWbGQ1VvwwbAyQno00d83bBB0yMiIiIiIiKqenmZOgDQ0qalBkdSNgzqULEcHEQPHZlMfC+TAWvXMnujtiqu3I4NsomIiIiIqLYrGNRpYfNMpo68iHIsLcGgDpVo4kQgIkLMfhURwSbJtVlJ5XZERERERES1WV5Qx87YDuYG5vkrIiMBW1tgwgTgxAnNDK4E7KlDz+XgwOycuiCv3K5gYIfldkREREREVNs9fvoYCRkJAIoovfrjDzHzlZ8f4OICdO9e7eMrCTN1iAgAy+2IiIiIiKhuKrGfTkoKYGwsnr/6ajWOqnQY1CEiFZbbERERERFRXVNiUOezz4DHjwF/f6Cl9jVQZvkVEalhuR0REREREdUlz53O3MAA8PauxhGVHoM6RERERERERFRnLfZajEkdJuHm45to36C9podTJgzqEBEREREREVGdpa+jjza2bdDGtk3+QoUCkEjEQ4uxpw4RUR0WFSV6KEVFaXokRERERERaZPt2oHlzYO5cIDxc06MpFoM6RER11IYNgJMT0KeP+Lphg6ZHRERERESkJX7/Hbh7F1i0CHj4UNOjKRaDOkREdVBUFDB5ssgqBcTXKVOYsUNEREREdUtAaAC+DPwS+27vQ2JGolioVAJZWYBUCjRoAHh6anaQJWBQh4ioDgoJyQ/o5JHLgXv3NDMe0qybN0WQb/NmTY+EiIiIqHrtvbUXX/37FYb+byiuP7ouFkokwKFDQEwMsGuXCO5oKe0dGRERVRk3t8LvTTIZ0KSJZsZD1U+pBI4cAQYMAFq1Av76SwT2iIiIiOqSS7GXVM/bNWinvrJ+faBbt2oeUdkwqENEVAc5OADr1olADiC+rl0rllPtlpkJbNwItG0LeHsDsbHAli1ARATw5puaHh0RERFR9ZEr5LgadxUA4GrhClN9Uw2PqOw4pTkRUR01cSLQv78ouWrShAGd2u7RI+Dnn4HVq4HHj4FBg4CVK4GePbV+pk4iIiKiKnE34S4ycjMAAO527mJhdjagp6fBUZUNgzpERHWYgwODObXd9evAjz8C27aJjKwJE4AZM0QJHhEREVFddjn2sup5e9v24smIEUB8PDB6NDBpEqCvr5GxlRaDOkRERLWMUgn4+wPLlgEBAYC9PfDll6IZsqWlpkdHREREpB0K9tNxt3MHEhJEg+TcXCAyEnj3XQ2OrnTYU4eIiKiWyMgAfvlFND4eMEB8Ltm2DQgPBz7+mAEdIiIiooLUMnUatAcePABatBALRo3S6lmv8jBTh4iIqIaLjQV++glYs0YEcl55RTzv3p39coiIiIiKolQqVZk6NoY2sDO2A9rbA1evAteuAaY1o2kygzpEREQ11NWrosRqxw5AV1fMXjVjBuDqqumREREREWmnzExg1izA2C4a8Yp4AEALfS8EBkrg5vZfv8k2bTQ7yDJgUIeIiKgGUShEqffSpcDffwOOjsDChcBbbwEWFpoeHREREZF2++svMSOoSUMF0O5dIKUhjp/8GH0gqq3WrROzxNYU2l8gRkREREhPFyVVLVuK6cjT0oBffwVCQ4E5cxjQISIiIiqNXbuA9u2BpEhHHJ31EySnPkFeaEShAKZMAaKiNDrEMmFQh4iISItFRwOffioycqZOBVq3Bk6eBM6eFf37dHU1PUIiIiKimiE9XWTqjBwpsnKkUjFraEFyOXDvnmbGVx4svyIiItJCly6Jfjk7dwL6+qK8avp0oHFjTY+MiIiIqGY6dEgEdkaOFN+7uYnAjkKRv41MokCTJjUn/6XmjJSIiKiWUyiA/fuB3r2BDh2Af/8FFi0SKcDLljGgQ0RERFQRu3YB7doBjRpn4fHTx3BwED10ZBIR1ZEhF2u/TxbNkmsIBnWIiIg07OlTYPVqoHlzYMgQMSvDb7+JfjmzZgFmZpoeIREREVHNlpGRX3oV9DAI9ZfUR8OlDZHZ5idERErR1FWOD7yvYeLsmtWokOVXREREGvLwIbBqFbB2LZCcDAwfDmzeDHh4aHpkRERERLXLoUPiRtrIkcChmEsAgOjUaOjKdOHgAHR9UYZ/77hreJRlx0wdIiKiahYcDIwZAzg7iwydCRNEVs5vvzGgQ0RERFTZoqLEjbTmzYGmTYHLcZdV69wbiEBO27bAtWvq/XVqAmbqEBERVQO5XPTLWbYMOH5cBHQWLwbefBMwNdX06IiIiIhqpw0bgMmTRbBGIhHfX8oRmTqOqVK0Nm8KQAR10tOBsDCgSRNNjrhsypSpc/78eUybNg2tWrWCkZERGjVqBB8fH9y9e7dU+yclJWHy5MmwsbGBkZERevfujeDg4HINnIiIqCZISwNWrgSaNQOGDRPBnd27xVSZ77/PgA4RERFRVYmKyg/oAGL68ilTlLhxLxkA8OvBeqjn3ASYNQttXZ8CAK5e1dRoy6dMQZ3vvvsOv//+O/r27Yvly5dj8uTJOH78ODp06IDr16+XuK9CocDLL7+MHTt2YNq0afj+++/x6NEj9OrVCyEhIRV6EURERNrmwQPgww8BBwdg5kygc2cgKAg4eVL0zpHJND1CIiIiototJKRwOZVcLkFuvBMckwCP20+B+Hhg3z7YOtdD/frAlSsaGWq5lan8atasWdixYwf09PRUy3x9fdGmTRssWrQI27ZtK3bf3bt34/Tp09i1axdGjBgBAPDx8UHTpk0xb9487Nixo5wvgYiISHucPy9KrH77DTA2FneHpk0DGjXS9MiIiIiI6hY3N0AqVQ/sSKUKKCzvQT8XuNunPZqfvCUaHEqlaNu2lmfqeHp6qgV0AMDNzQ2tWrXCrVu3Stx39+7dsLW1xauvvqpaZmNjAx8fH+zbtw9ZWVllGQoREZHWkMuBPXuA7t2BF14QGTnLlolsne+/Z0CHiIiISBMcHIB33sn/XiYDer23EzB7iHtWwMN1S4DoaGDqVABAu3a1PKhTFKVSibi4OFhbW5e43aVLl9ChQwdIpeqnfOGFF5Cenl7qvjxERETaIjUVWL5c3AUaPlw039u7F7h7F3jvPcDERNMjJCIiIqrb0tJEb8N//gEiIoDsdj+r1rVv0B6wtAQsLACIZslhYeIzXk1R4aDO9u3b8fDhQ/j6+pa4XUxMDOzs7Aotz1sWHR1d7L5ZWVlISUlRexAREWnK/fvA7Nni7s/s2UDXrqLs6vhxYOhQ9sshIiIi0hanTgH9+wO9egH2DRW4E38HAOBk5gQrQyu1bdu2FV+f0zJYq1RoSvPbt29j6tSp8PDwwLhx40rcNiMjA/r6+oWWGxgYqNYX59tvv8VXX31VkaESERFV2Nmzoqzq999FFs4774h+OQ4Omh4ZERERET3ryRMx4+gLL4jvpRIpoifdxr2zB3HftXC1UYsW4ubc1auAh0c1D7acyp2pExsbi5dffhlmZmbYvXs3ZM+5LVmvXr0i++ZkZmaq1hdn7ty5SE5OVj0ePHhQ3mETERGVSW4usGsX4Okp3tyDg0XJ1YMHwKJFDOgQERERaatz58TXLl3yl+n8thvNB4xF/+EfAn//rba9vj7QvHnN6qtTrkyd5ORkDBgwAElJSThx4gTs7e2fu4+dnR1iYmIKLc9bVtIx9PX1i8zyISIiqiopKcCGDSKAc/++SNndtw8YNEjMokBERERE2u3cOdEyx9W1wMING8TXa9cAM7NC+9S0GbDKHNTJzMzE4MGDcffuXRw9ehQtW7Ys1X7t27fHiRMnoFAo1JolBwUFwdDQEE2bNi3rUIiIiCpdeDiwYoV4v8/IAEaNEjNbdeig6ZERERERUVkEBYnSK4nkvwVKpZjpSiIBMjOL/IDXti1w4IDYVLWfFivTvUa5XA5fX1+cOXMGu3btgkcxRWYxMTG4ffs2cnJyVMtGjBiBuLg47NmzR7UsPj4eu3btwuDBg5mJQ0REGqNUAqdPAyNGAE2aAFu2iNmr7t8Htm5lQIcq5vz585g2bRpatWoFIyMjNGrUCD4+PqWa+dPPzw8SiaTIR2xsbDWMnoiIqGZSKkWmTl4/nUdPH2HwziH4pnEUgveuBgIDi4zatGsnMrbv36/e8ZZXmTJ1PvjgA/z5558YPHgwnjx5gm3btqmtHzNmDADRA2fz5s0IDw+Hs7MzABHU6dq1KyZMmICbN2/C2toaq1evhlwuZxNkIiLSiNxc0fR46VLxpt+0KfDTT8AbbwCGhpoeHdUW3333HU6dOoWRI0eibdu2iI2NxapVq9ChQwecPXsWrVu3fu4x5s+fj8aNG6stMzc3r6IRk6ZERQEhIYCbG/t1ERFVVHg4EB+f30/nbNRZ/HX3L/x19y8kZiSig/fiIvfLmwHr6lXgv3CGVitTUOfy5csAgP3792P//v2F1ucFdYoik8lw8OBBzJkzBytWrEBGRgY6d+4MPz8/NGvWrGyjJiIiqoCkJGD9emDlSiAyEujTB9i/Hxg4kP1yqPLNmjULO3bsgJ6enmqZr68v2rRpg0WLFhW6SVaUAQMGoFOnTlU5TNKwDRuAyZMBhUL8HVq3Dpg4UdOjIiKqufKaJHfuLL6eeXBGtc7DsfipreztRR+eq1eBIUOqcoSVo0wfXQMDA6FUKot95PHz84NSqVRl6eSxsLDA+vXrER8fj6dPnyIwMJAfUIiIqNqEhgIzZgCOjsAnnwC9ewOXLwPHjrEBMlUdT09PtYAOALi5uaFVq1a4detWqY+TmpoKuVxe2cMjLRAVlR/QAcTXKVPEciIiKp+gIMDFBbCxAaBQoNH632CfItZ1deha7H4SSc1qlsyPr0REVKsplcCJE8Crr4qShu3bgZkzRYaOn5+omyaqbkqlEnFxcbC2ti7V9r1794apqSkMDQ0xZMgQhISEPHefrKwspKSkqD1IO4WE5Ad08sjlwL17mhkPEVFtULCfjjzAH+/8Lwz3lwGLg8xgb1LyDN4M6hAREWlYTg6wY4d4M+/RA7h9G1izBnjwAJg/H2jQQNMjpLps+/btePjwIXx9fUvcztDQEOPHj8dPP/2EvXv34sMPP8SxY8fg6emJBw8elLjvt99+CzMzM9XD0dGxMl8CVSI3t8KZgjKZaNxORERll5MDBAfn99NJXS765+goAUmr5/eya9tWBNzT06tylJWDQR0iIqpVEhOB774DGjcGXn8dsLAADh0Crl8X5Q316ml6hFTX3b59G1OnToWHhwfGjRtX4rY+Pj7YtGkT3njjDQwdOhQLFiyAv78/EhIS8PXXX5e479y5c5GcnKx6PC8IRJrj4CB66Mhk4nuZDFi7ls2SiYjK69o1MWN5XqbOvql9Mb8HcN4e0H3l1efu37atyKC8ebOKB1oJytQomYiISFuFhADLlwObNolZrcaMAd5/H2jTRtMjI8oXGxuLl19+GWZmZti9ezdkeVfxZdCtWzd06dIFR48eLXE7fX196Ovrl3eoVM0mTgT69xclV02aMKBDRFQRQUGAjg7g7i6+P5ZzB1v7APN6A2edX3zu/nlzOd27B2h7G2AGdYiIqMZSKoF//wWWLROzV1lbA3PmAO+8A9jaanp0ROqSk5MxYMAAJCUl4cSJE7C3L7mevySOjo64c+dOJY6OtIGDA4M5RESV4dw5kW2Tl6F9JkrMfKWvow93O/fn7m9iIoJCCQlVOcrKwaAOERHVONnZwG+/AUuXApcuAS1bitKFMWMAAwNNj46osMzMTAwePBh3797F0aNH0bJlywodLywsDDY2NpU0OiIiotolKAjo1Us8f/z0Me49EZ3nO9p3hJ5Mr/gd/yORAFZWDOoQERFVqidPRJ+JlSuBmBhRqnD4MODtLd58ibSRXC6Hr68vzpw5g3379sHDw6PI7WJiYpCcnAxXV1fo6uoCAB4/flwoeHPw4EFcvHgR06dPr/KxExER1TTJyWKCjA/nKIHXRsO4Xy9seOlnnI67gJY2pb+pYmUlPntqOwZ1iIhI6929C/z4o5iCXKEAxo4V/XJatdLwwIhK4YMPPsCff/6JwYMH48mTJ9i2bZva+jFjxgAQjY03b96M8PBwODs7AwA8PT3h7u6OTp06wczMDMHBwdi4cSMcHR3xySefVPdLISIi0noXLogS/T6Ko8DOnai3cyfeHDIEb+7bV6bjMFOHiIioApRK4J9/RL+cv/4C6tcHPv4YePtt8Zwqj1KpRHJWMswNzDU9lFrp8uXLAID9+/dj//79hdbnBXWK4uvriwMHDiAgIADp6emws7PDpEmTMG/ePNiycRQREVEh584BpqaA4/VD+QvHji3zcWpKUEeiVCqVmh5EWaWkpMDMzAzJyckwNTXV9HCIiKgSZWUBO3eKYM6VK2L2qpkzgddeY7+cyhSXFocjYUfgH+qPI6FH4OnoiT2+eyr9PHzP1h78WRARUV0wdCiQlgYcPQrg9Glg82Zg9WqgjDNOvvWWmBo9KKhKhlmisrxnM1OHiIi0Qny86JezahUQGwsMHAgsWQL07ct+OZUhMzcTJyNPIiA0AAGhAbgSd0Vt/bHwY8hV5EJHyo8GREREVDMplSIIM2GC+P5haydcnT0UXbKTYVnPskzHsrSsGZk6/ORGREQadfu26JezebP4/o03RL+cFi00Oara58MjH2LluZVFrjPSNUIPpx5ISE+ArTFLeoiIiKhmevhQ3Bzs0kV8v/f2Xrx36D0AwIYhG/Cm+5ulPhYbJRMRERVDqQSOHRMlVgcPAg0aAJ99BkyZAlhba3p0NVd8ejyOhh1FQGgAvun7DRoYN1Ct6+fSTxXUkUCCDnYd0N+1P7xdveHh6FGq6T2JiIiItNnFi4AhnqJTJyMAIhM5Twe7DmU6lpUVkJQEyOVlrtyqVgzqEBFRtcnKAnbsEMGca9eAdu1Eho6vL6Cvr+nR1TzZ8mycfnBaVVIVHBMMJUSrvN7OvTG2XX5TwN7OvTGh/QR4u3qjb+O+sDGyKe6wRERERDXSjbOpiJQ0huUcb+TOeh//hP8DALA2tEZb27ZlOpaVlbgRmZio3TcdGdQhIqIq9/gx8PPPokddXBwwaBCwfDnQqxf75ZTV3YS7OHzvMAJCAxAYEYinOU+L3O5o+FG1oI6Jvgk2vrKxuoZJREREVO1s/1oPK2UC8OuvSFSkIblFMgBxc0sqkZbpWFZW4mtCAoM6RERUR924IfrlbN0KSKXA+PHAjBlAs2bVc/6oKCAkBHBzAxwcquecVW3KX1MQGBFY5Lr2DdrD28Ub3q7eeLHRi9U7MCIiIiINuxttgjRDGxinP8bel12AMLG8b+O+ZT6W5X99lbW9WTKDOkREVKmUSiAgQJRY+fsD9vbAvHnA5Mn5dzyqw4YN4pwKhQgorVsHTJxYfecvrxx5DoIeBiEgNADnHp7DwdcPqt1Z8nbxVgV1bI1s4e0qgjheLl5sckxERER1VkIC8P2Tt9Bx42vwMfPHb2mrVev6upQ9qFMwU0ebMahDRESVIiMD2L5dZObcuAF06CAydHx8AL1q7sEbFZUf0AHE1ylTgP79tTNjJ/RJKAJCA+Af6o+/w/9Ganaqat2V2Ctwt3NXfT+sxTDoynTh7eqNNvXbQML6NSIiIiJcuSK+tvUwQmaTgTj13esAgEZmjeBq4Vrm4+UFdbR9BiwGdYiIqELi4kSvnJ9/BuLjgSFDgJ9+Anr00Fy/nJCQ/IBOHrkcuHdPO4I6uYpc7L+zXzQ4DgtAWGJYsduee3hOLajT3Lo5mls3r45hEhEREdUYly4Bhoai7P7fyNPIzM0EIEqvynMTTE8PMDZmpg4REdVS166JEqvt2wEdHeDNN4Hp08Ubqaa5uYmSq4KBHZkMaNJEc2MqSCqRYspfU/A4/XGhddaG1ujn0g/9XfvDy8ULDU0bamCERERERDVETg6wdSuuXxiNtm0NIJMBx8LypzIvTz+dPFZWDOoQEVEtolCIPjlLlwJHjwINGwILFgCTJgEWFpoeXT4HB9FDZ8oUkaEjkwFr11Zvlk5EUoRqqvFcRS7+GPWHap1UIoWXqxd2XNsBXakuujXqpuqN075B+zLPzkBERERUZ23bBkyciEU6n2NPv58BDEEjs0boZN8JwTHB6NO4T7kPbWnJoA4REdUCGRmiP86yZcDt20CnTsCOHcCIEYCurqZHV7SJE0UPnXv3RIZOVQd0UrNSERgRqOqNE/IkRLVOV6qL1KxUmOibqJbN6DIDo1uPRk/nnjDWM67awRERERHVRgoF8M03AADb3Gg0bCsa4UzpNAVTOk1BcmYyzAzMyn14ZuoQEVGNFhOT3y/nyRNg2DDgl1+AF1/UXL+csnBwqNpgTnx6PNZdXIeA0ACcfnAaOYqcIrcz1jPG3YS76GjfUbXshYYvVN3AiIiIiOoCqRT47Tc8mbUAFwNTYTfiRbXVFQnoACKo87hwtbxWYVCHiIgKuXJFZOXs2AHo64t+OTNmAC4umh6ZZuUqcqEjzX/rlCvk+PTvTwttpyPVgaejJ7xdRElVB7sOkEll1TlUIiIiorrB3R27X9uD949n4Umbyj20lZXIUtdmDOoQEREAkb166JDol/P334Cjo8hmfestwNxc06PTjKfZT3H8/nH4h/ojIDQAvZ1746eXf1KttzW2RfsG7XE59jLcLN1UfXF6OfeCqb6pBkdOREREVHdcugS4ttSHgQEQmRwJR1PHcs149SyWXxERkdZ7+hTYsgX48Ufg7l2gSxdg505g+HAxq1VdolAqcCX2imqq8ZORJ5Etz1atL6q8as3La1DfqD4aWzSuzqESERER1V0KhSi9+s+lS0D79kBsWiycfnRCI7NGmNJxCj7p/kmFTsNGyUREpLWio4FVq8SsUElJwKuvAn5+gIeHpkdW/YJjgrH0zFIcCTuCR08fFbmNVCKFjaFNoYbHXRy6VNcwiYiIiAgAli8XU7IuWgR5m/a4ehXw8QH+Dv8bgMjWSc1KrfBprKzEhCEZGUC9ehU+XJVgUIeIqI4JDhb9cv73P8DAQMwSNX060LiOJJpk5GRAoVTASM9ItSw5Mxnbr20vtK2zuTO8XbzRv0l/9GncB+YG5tU4UiIiIiIqJCkJWLhQzOJx9CjCAsKRkeEId3dgW9gx1Wb9XPpV+FRWYjItPHkCNGxY4cNVCQZ1iIjqAIUC+Osv0S/n338BJyfgu+9EQMe0lrd+USqVuP7oumqq8eP3j2NZ/2V4p/M7qm08HT1hqGsIqUSKPo37qBocN7FsUin12ERERERUScLCABMTEWkZPRrnYx0BAG3bKnFkyxEAgL5MH56OnhU+VV5QJyGBQR0iItKAp09FSdWPPwL37onSqt9+E1OT1+Z+OXFpcTgadhQBYQEICA1AbFqs2nr/UH+1oI6+jj6C3gpCM6tm0JXpVvdwiagWiooCQkIANzfAwUHToyEiqkU6dADu3AFWrwaGDcPl1eKGZWT2ZTxIeQAA6OXcC/V0K14vVTCoo61q8Ud6IqK6Kyoqv19OaiowYgSwbZtoglybbb2yFUvPLsXl2MvFbuNo6ojG5oVrzVrXb12FIyOiumTDBmDy5Pw+nuvWicxIIiKqJPr6wMyZAESTZHd34I/bf6hWD20+tFJOY2kpvjKoQ0RE1eLCBdEv57ffAENDcVHx3ntAo0aaHlnlUiqVuB1/G66WrtCT6amWJ2clFwroGOoaordzb9V0482smrGkioiqTFRUfkAHEF+nTAH692fGTm3GzCwizVAqRVDnvfeAPXf+UC0f0mxIpRzf3FwE5xnUISKiKiOXA3/+KYI5J06IhsdLlgBvvinKjWuLhPQEUVL133TjUSlRCBwXiJ7OPVXbeLt6AwA62HVQ9cXxdPSEvo6+poZNRHVMSEh+QCePXC5KYHmxXzsxM4uommzfDgwerNYQ8uFDEXCxbxmOqzevAgBeaPgC7E3sK+WUUilgYSHa92grBnWIiGqo1FRg0yYxo2NYGPDii8CePcCQIYBMpunRVVy2PBtno86KIE5oAC5EX4ASSrVtAkID1II6bpZueDT7EWyMbKp7uEREAESmhlSqHtiRyYAmTTQ3Jqo6zMwiqiZHjwJjxgDW1uLu5bhxAESWDgBEGe9TbTq02dBKPbWVFTN1iIioEj14AKxcKe4EpqUBPj7Azp1A586aHlnlmXpgKrZc3YK07LQi1xvoGKCnU89CfXAkEgkDOkSkUQ4O4u/zlCkiQ0cmE/3NeIFfOzEzi6gaKJXA3LnieXy82qpLl0TfG33jDJjpmyE5K7nS+unkYVCHiIgqxblzYkry3bsBY2NxwTBtGuDoqOmRlV9iRiLOR59XlU3lUSgVhQI6bW3bwtvFG16uXujeqHulzGhARFQVJk4UmRr37okMHV7c117MzCKqBhKJSEefPVvc3Rw7VrXq8mWgfXvgk+5zMcdzNk4/OI3m1s0r9fQM6hARUbnJ5cAff4hgzunTgKurmJ58/HgR2KlpchW5OPfwnKqkKuhhEBRKRaGSKW9Xb+y5vQdeLl7wcvGCt6s37EzsNDhyIqKycXBgMKcuYGYWUTVxdAT+9z8gPV1EUv9z8SLg6yue68p01cryK4ulpQjSaysGdYiItFBKCrBxo+iXExEB9OghgjuDBtW8fjnhieHwD/VHQGgAjoUfQ0pWSqFtjoQdweg2o1XfD242GK80fwVSibTQtkRERNqEmVl1D2c70yBDQ9XT+HggMhLo0KFqT2llJTLmtRWDOkREWiQiQvTL+eUXICMDGDUK+P33qn+zqgq5ily0+bkNbsffLnabljYt4e3ijZY2LdWW60j59kRERDUHM7PqDs52Vo3i4gAbG7XMnIKCgwHIstGsbRaAqpvyleVXRET0XGfOiCnJf/8dMDMDpk4V/XIaNtT0yJ5PrpDjYsxFRKdGqzWm05HqwNrQWm1bq3pW8HLNL6lyMOUnYCIiIqoZONtZNZLLgYEDAR0dYNWqImcEuXgRMGhzCB57fNHvcj989OJH6O7UvdKHYmUlpjRXKkV7H23DoA4RkYbk5oqeb8uWAWfPihTelSvFDI1GRpoeXckikyNVfXGOhh1FYmYibI1s8UqzVyAp8G43yG0QZBIZvF290d+1P9zt3FlSRURERDUSZzurRmvX/peKAxFJCw4uFFEJDgbMuvyBOHkWDoQcwLud362SoVhZiZ9zcjJgbl4lp6gQBnWIiKpZcjKwfr0I4Ny/D/TqBezfL25GFJNdqnFp2WkIjAjEkdAjCAgLKLKkKu5pHK49uoa2tm1Vyz7q9hE+6vZRdQ6ViIiIqEpwtrNq1LEj0KoVcOOGaDJZRIrMheBcJI/ZDwAw1jNGn8Z9qmQolpbia0ICgzpERHVaWBiwYoWoxc7KAl57DZg5U0zDqM1ux99G25/bIkeRU+R6cwNz9HPpB28XbzQ0qQH1YkRERETlwNnOqlGXLiIV5/BhMWPIMxITgQj5KUAqmt281OQlGOgYVMlQrKzE14QEMROttmFQh4ioCimVwKlTosTqjz9EdH/6dNEzx95e06NT9zDlIY6EHYFVPSsMbjZYtdzN0g2m+qZIyBBvmjKJDF0duqpKqjrZd4JMWsOm5CIiIiIqB852Vo309IAhQ4pcFRwMoO121fdDmw2tsmHkBXWePKmyU1QIgzpERFUgJ0c0PV66FDh/HmjWDFi9Ghg7Vm0mRo1Kz0nHifsnEBAaAP9Qf9x4fAMA0KdxH7Wgjkwqw/j245Gekw5vV2/0du4NMwMzTQ2biIiISKM421kVyc0VjZFL4eyFDKD1/wCI0quCk3VUtoKZOtqIQR0iokqUlCSmI1+xQsyQ0LcvcOAA8NJLmu+Xo1QqcTXuqmhwHBaAE/dPIEueVWi7k5En8TT7KYz08rs1L/FeUp1DJSIiIqK6Zvx48YF52bL8SEox/gr9A2iYAgAY2XKk2ufWymZoCBgYMKhDRFSrhYaKHm4bN4osndGjRb+ctm2fv2912XFtB8bsHVPkOqlEis72nVUlVVVVk0xEREREVMihQ8D2/8qpzp0TDZJlxZf3X5H4qZ6Pbz++ascG0SyZQR0iolpGqQROnBAlVn/+KW4ozJoFvPsu0KCBZsaUmZuJk5EnERAagMFNB6O7U3fVut6Ne6tt28isEbxdvNG/SX/0adwHlvUsq3u4RERERERAaipgZiamif3ooxIDOjejopBhdwQA4GLhgm6NulX58KysGNQhIqo1cnKA334TmaEXLwItWoiZD8aMAerVq96xKJVK3Hx8E/6h/ggIDcC/9/9FZm4mACArN0stqGNvYo+pnaeiuXVzeLt6w83SDZIipockIiIiIqpWPj5A9+7A+vWiDKsEJy89Ah6+ADgEYVy7cZBKqr7HgZUVGyUTEdV4T56IaSxXrQIePgS8vUWmaP/+QHXGRuLT43E07KgqkBOdGl3kdkfCjhRatmrgqqoeHhERERFR2dnZAZ9//tzNUu92QL3tZxEUdgv1jasn01ybM3U03LaTiEj7hYSIKcgdHYEvvxRNj69dA/z9xfPqTnb5/tT3eO331+B32a9QQKehSUNMaD8BO4fvxPEJx6t3YERUpPPnz2PatGlo1aoVjIyM0KhRI/j4+ODu3bul2j8pKQmTJ0+GjY0NjIyM0Lt3bwQHB1fxqImIiKqYQlGu3YKDgfbtgTYNWsDW2LZyx1QMbQ7qMFOHiKgISiUQGChKrP76C7C2Bj78EHjnHaB+/ao+txJ3E+6qZqlaOWAlnM2dVeu9Xb2x+PRiAEA9nXro6dwT/V37w9vVGy2sW7CkikjLfPfddzh16hRGjhyJtm3bIjY2FqtWrUKHDh1w9uxZtG7duth9FQoFXn75ZVy5cgVz5syBtbU1Vq9ejV69euHixYtwc3OrxldCRERUSeRywMtLPD78sMQeOs+6eFHsVp3YKJmIqIbIzgb+9z/R/PjyZaBVK1HaO3q0mMqwqjzJeIJjYcdUgZzI5EjVusFNB2Nyx8mq77s16oYPPT+Et6s3Xmz0ImeqItJys2bNwo4dO6Cnp6da5uvrizZt2mDRokXYtm1bsfvu3r0bp0+fxq5duzBixAgAgI+PD5o2bYp58+Zhx44dVT5+IiKiSrdoEfDPP+Jx8yawdetzd0nPScfpsCu4c7crPvqoem9iMlOHiEjLJSQAa9YAP/0ExMQAAwYA338P9OtXdeVVZ6PO4mDIQfiH+uP8w/NQQlnkdmeizqgFdQx0DPCd13dVMygiqnSenp6Flrm5uaFVq1a4detWifvu3r0btra2ePXVV1XLbGxs4OPjg23btiErKwv6+vqVPmYiIqpeqaliRu+7dwGpVDxkMjEJh48P0Ly5pkdYyXJyxIsEgLffLtUuf9z+A6/veR2Y2gzpDZcCGFh143uGlZX4GeXkALq61XbaUmFQh4jqtDt3gB9/BDZvFiVXb7wBvP++mNGqqi04vgAHQw4WWq4v00cPpx7wdvVGf9f+aF2/+NIMIqqZlEol4uLi0KpVqxK3u3TpEjp06ACpVL0N4gsvvIB169bh7t27aNOmTZH7ZmVlISsrS/V9SkpKxQdORESV6tYtcVNxyxYgPR1wcxOfSRUKUaH05Akwbx4waBAwaxbQq1f193OsEl9+CfTtKxrkvPhiqXbxu+wnnljfQQtX4yobWlGsrMTXJ08A2+pp41NqbJRMRHWOUgkcOybeHJs3B/buBT75BIiMFFOTV1ZAJzkzGXtv7cU7f70D97XuyFXkqq33dvFWPW9l0wqzus7C4dcPI/GjRASMDcBsz9loY9uGPXKIaqHt27fj4cOH8PX1LXG7mJgY2NnZFVqetyw6uujZ7wDg22+/hZmZmerh6OhYsUETEVGlCQkRGeEtWwK7dgEzZgARESLIc/u2yNgJDQViY4GNG8W6Pn2Ajh2BAwc0PfpK0r27eOGlcPPxTdXMrvpPXdDLpVtVjqyQvKCONpZgMVOHiOqMrCzg119F8+OrV4G2bQE/P2DUKKAyqhdyFbk4//C8qi9OUFQQ5Eq5an1QVBBebJR/J2Jo86EwNzCHl6sX7E3sKz4AIqoRbt++jalTp8LDwwPjxo0rcduMjIwiy6sM/mvylZGRUey+c+fOxaxZs1Tfp6SkMLBDRKQFAgIAX1/AxkaUXA0fXvxnUX19YMIEYPx44OhR0Ypm0CDRJmD27BqWtZOSApialmvXJaeXqJ53VEyDVFK9+SkM6hARadDjx/n9cuLigJdfFoGd3r0r/kaYq8jFxksb4R/qj2Nhx5CclVzkdrpSXdxJuKMW1HEyd8K49iVf0BFR7RIbG4uXX34ZZmZm2L17N2TPme2jXr16aiVUeTIzM1Xri6Ovr89+O0REWkSpFGX/s2cD/fuLm41mZqXbVyIRMz717Qt88YWYMCo8HFixAtCpCVf1Bw+KPgdbtgADy9YLJzo1Gtuu/jepQKYZXmv6VhUMsGSWluIrgzpERNXo5k3xxrl1q3gjHDdOZHhWpNGcQqlQuzMgk8jw9Ymv1WarytPcurlqqvEeTj1grFe9tb9EpF2Sk5MxYMAAJCUl4cSJE7C3f36Gnp2dHWJiYgotz1tWmmMQEZHmZWWJfsB+fiIg8803ZZrFW0UqBRYuBJydxfEiI4GdOwFjbf6YGREBjBkDJCaKu6vHj4vSq1JaEbQCOYoc8c35d/Cij0nVjLMEeUGdJ0+q/dTPxaAOEdUqSiVw5IjIxDl8GLCzAz77TLzp5aVNloVcIUdwTLCqpCpXkYtTb55SrZdIJPB28cb6S+thWc8S/Vz6wdvFG96u3nA0Y5kDEQmZmZkYPHgw7t69i6NHj6Jly5al2q99+/Y4ceIEFAqFWrPkoKAgGBoaomnTplU1ZCIiqiRZWWJm1dOnxc3GMWMqfsy33gIcHYGRI4GePQF/f8DauuLHrRIWFqLD8969wCuvAN1K3w8nNSsVay6sAQDIoAe9q9PRWgNziOjoiKwqZuoQEVWRzExRk7xsGXDjBtC+vcju9PUF9PTKdqwHyQ9wJOwIAkIDcDTsKBIy8v96SyBBQnoCrAzzI0QzPWZiUsdJ6GjXETJpOW65EFWXiAggJgZIShI53AXztaOiAAcHTY2sVpPL5fD19cWZM2ewb98+eHh4FLldTEwMkpOT4erqCt3/5ksdMWIEdu/ejT179mDEiBEAgPj4eOzatQuDBw9meRURkZZTKoFJk4BTp8SNxx49Ku/Y/fsDJ06It/RRo8QNTa0sxTIzA37/XfRDeO21MvU/+CX4F1V7A4eEsWjcyk5jU4pbWTGoQ0RU6eLigJ9/BlavBuLjReO4VavEHYuy9MuJTYvFopOLEBAagFvxt4rdrollE9xPvq8W1GlpU7o77kSVIioKuHxZBGY8PABX1/x1qanAq6+KdR06iOncCpo6VdS0A6LZVMFbeomJDOpUkQ8++AB//vknBg8ejCdPnmDbtm1q68f8d8t27ty52Lx5M8LDw+Hs7AxABHW6du2KCRMm4ObNm7C2tsbq1ashl8vx1VdfVfdLISKiMvr6a5Gds3175QZ08rRrB/z2m5hJ6+OPgSVLnr+PRkgkwDvvlGkXuUKOH8/+qPo+NeADvDiiksdVBgzqEBFVouvXRVbOtm3ijsT48aJfTmkqERRKBdKy02Cqn99930DHACvPrYRCqVDb1kzfDH1d+qK/a394uXihsUXjSn4lVOspFOI2XcHC+adPgd27RfClUSNg2DD1fd58E/j3X7E+NhZqt6QOHQImTxbP169XD+ro64upMQDgv9mR1Jib5z9PTtbiPO3a5fLlywCA/fv3Y//+/YXWjykhD18mk+HgwYOYM2cOVqxYgYyMDHTu3Bl+fn5o1qxZVQ2ZiIgqwa+/Ap9/Dnz1FTB6dNWdp1cv4IcfgPffF1Oev/Za1Z2r1AICxM0nk/L3v5FJZfjztT+x5PQSPE7KQMCdFnjxxefvV1UsLRnUISKqEIVC1AsvWybSV+3txZvk5Mn5zcuKE5MagyNhR+Af6o8joUfwctOXsemVTar15gbm6NKwC4IeBqGrQ1d4u3jDy9ULLzR8ATpS/qms03JzRQAEKNyYacsWIDRUBGmevTW2Zo24ZZaSAuzaJeYrzZOeLiKRgEgvezaoExcHhIWJ5ykp6uctOE1GUpL6fnp6QL16oh5RLi/8Wl56Scyfam5e+EOWm1vh7alSBAYGlmo7Pz8/+Pn5FVpuYWGB9evXY/369ZU7MCIiqjKnT4upyMeMEYGdqjZ9OnDxIjBxItCihWhFoDH//CMaIrdsCezfL25glVP7Bu2x7dVt2OSnQACArl0rb5hlZWUFPHyoufMXh1cqRKT1MjJERs6yZcCtW+IOxLZtojFccf1yMnIycCLyhGhwHBqAa4+uqa0PCA2AUqmEpECN1sZXNqKBcQOYG5hX4ashjYiPF+VFT58W/pRz5Aiwb58IkMyZI/KY84SE5Kd/jRkj8qcLWrMGOHNGPF+0SL2QXSrNDwY9G3wpKTADiMCLtbXYLiNDfV27dsCCBSIwU1Sjwbg4wMhInP9ZY8eKR1GKyuwhIiKiMrt/X/QDfuEFkVRblpYA5SWRiKrr69fFvaILF8o3SUiF5eaKLs65ucDVq8C6dWK6rgo6e0aKVq1Ez2VNsbISL0nbMKhDRForNlb0yvn5Z5Hq+Mor4s2qW7fi3xzPPDiDL//9EsfvH0dmbmaR2xjrGaOTfSekZKXAzCD/4rq5dQXmOqeqFxMjPiUlJQGdO6t/UomIAObOzW8APGuW+r49eoiIoImJyHwp6NIl4KefxPNXX1UP6jwv+PJsOVPBMdnZiYCQubn6cQARjfz5Z8DUtOg+NkVka6g0ayamdCtOBdKciYiIqGIUCpGMa2gI7NkjKqOrS716YoKpTp3EvaiDB6snoKRGR0ec+OWXxeegL78s8yHkCjkyczNhpGekWnbqFODpWYnjLAcTEyAtTbNjKAqDOkSkda5eFVk5O3aIViJvvilSSps0Ud8uLi0OujJdWNbLr72SSCQICA1Q204CCTo37KyaaryrQ1foyjTUNr+uS0wU+chJSeIH2qWL+vrRo4F798Snkn//VV+3ciXw7bfi+dGjQN+++euys4GdO8XzovrE5AVVUlPFnaOCGTUlBW7MzEQ9uLm5CCQ96/PPxS9nUeVMgweLR3Hefrv4dURERFQjrVwJBAYCf/+tmdZ1Tk7A5s0iprJ1K/DGG9U/BjRrBgQFiQ/y5ZiOa9PlTfgy8Et80/cbjGk7BinJUty4IRKqNcnQUFTQaxsGdYhIKygUov/rsmXAsWOAo6PI1Jw0KT8ZIis3C6cenEJAaAD8Q/1xOfYylngtwQeeH6iO09m+M8wNzGGiZwJvV294uXihn0s/tdmqqAwUCtGb5dm5I3/9VcyepKdXODjx7bfAhg0iQHLkCODunr/u7l3RQwYA3nuvcFDn0iXg9u2is00KZsU8G3x5NmPmWf37i4bC5uaFgzpDh4qaPnNzkV1TkL6+CEIVp5ipqYmIiKjuuX1btNObPh3o3Vtz4xg4UNwnmzlTtNOrX7+KT5iTU/izYjlrv1KzUvHZ358h7mkcxv0xDs2tmyPh6gsAoNEmyYCobn/6VLNjKAqDOkSkUenpotfsjz8Cd+6IZIhffxU9ZXV0lLgdfxt+Z/0REBqAf+//i/Qc9fC4f6i/WlBHJpXh5rs30cC4gVq/nKoSFSXarri5aels0Lm5otwoK6twwOLgQXEXJTlZlC7Z2uav+/dfEexITs6ftqGgWbNEfZyjY+GgTkqKaB4MiMycgkoKzOStl0hE0EUuV58x6oUXxKckMzNxB6ggKytxTnNzUdL0rJJSf21t1V87ERERURnl5gLjxomewHmJxZr044+iYfLMmWI69bIo0+fbjAwROerXT5SHV/Dz97cnv0Xc0zgAwPAWw/FCwxfw+RrRbrDghJ+akJepo1RqoKytBAzqEJFGREeLNiZr1ohr+6FDRXKHp+d/jd4urMXCEwsRlRJV7DHcG7jjRcfCIXs7E7sitq58GzaImbcUCtGTdt06MeNApUtKElkxyclA27bq3aGvXRMnTkoCRowQjYcKMjUVb7Zt2hTu7LZ/v/gBACI3t2Bgw8AgP+hSXPAlNrborBgrK/EwMxPvegXZ2QHz54v9W7cuvO+xY+LcRTX57dVLPIoikwEuLkWvIyIiIqpiixaJ5sSnT4uLf02zsQGWLhWBptdfF9k7pVGmz7dKJeDjAxw/Lh65uYVvBJZBRFIElp5ZCgDQk+nhe6/vAYh+Oi++qPlAiqGheMlZWdo1vwSDOkRUrS5fFiVWv/4qKlsmvJUNT58zGNKpMwx1898BdaQ6hQI69ib28Hb1hreLN/q59IONkU01jz5fVFT+Gx4gvk6ZIqp8iryjkZgoSo+SkkTTuMaN89fJ5WJGoqQkwNlZdIcu6O23gf/9TzwPC1PfNzoaWLVKPHdxKRzUyZs9qajgS0klS1ZW4vaMuTnQsGHhfRcsEO9oBY+RZ/Zs8SiKqWnJ83pqw6cgIiIiojIIDhaxjLlzC1eWa9LYsWLG2LffBm7ceP5cCmX+fCuRAAMGAH/9JQ5eUi/BUph7bC6y5FkAgBldZsDFwgU5OSKxvBz9litd3sfU9HQGdYiojlEogAMHxN2CwEAl7Frfw6CvApDh4I9NUf9gZUAaDtkcwktNXlLt4+XqBQMdA/R06qnqjdO6futqKakqVk6O6BGTlISQSFcoFOrv2nI5cM/rHTgo/hbvPgUDHv7+wGuviedLl4pc2DwymZgeIStLZOI8q7S9ZIrKqOnVSywvKjDzxhtiVigLC6BlS/V1TZqIIFRxRowofh0RERFRHZGbC0yYIBKQv/hC06NRlzfNeevWojJq+fKStw8JyQ/o5JHLxRwWxZZhvfuu2KlNGzHtVjmdeXAGO6+LSS+sDa3xafdPAYhE8/R0zffTAdSDOpaWJW9bncoc1ElLS8PixYsRFBSEc+fOITExEZs2bcL48eOfu6+fnx8mTJhQ5LqYmBg0aNCgrMMhIi329Knovv/D6kSEKf5Gfc8A2HgFICYnAnuzAYTlbxsQGqAW1Glk1ghJHyVBX6cC80AWlx95+LB410pKAj75RL1vy++/Ax99JDJXfvhBfcoAuVxMJQDArctwSKW71d74ZBI5mtzeD+ChOHbBgEtpesnExRW9rmtXMX+imVnhqbFbtwbOnRP7F9UF79dfCy/L06KFeBARERFRufz8s6iGP39evUJeWzRuLCrf58wRZVgvvFD8tm5uouRK7fOtrPAMtIVMm1ahMeYqcjHj8AzV9/N7zYeZgfjMe+qU+Hft0KFCp6gUBYM62qTMQZ34+HjMnz8fjRo1Qrt27RAYGFjmk86fPx+NC5YPADAvKoWfiGqkhw9FRdDatUBirzeAEdsBiQKPACBHfdv6RvXh5eKFPo37FDqOvkRHlC2lpIj5GQs6f16k/yQlidzSjh3z1z1+LBrpJieLNNA//lDf98cfReYMIN6ELCzy1+Xk5Df5TUhQ38/AQNSMZWXBISME69aJlNS8fr5rX9wOh+MPReDl2db4TZrkN/ktqi/MuXOipf6zQRsAGD9ePIpiZFT0VNtERFRhWVlARISofM176OoC77xT+G2JiOqeR49EVfnkyeofRbXNjBliYpKZM4GTJ4vvTePggMKfb9cWyNJJTQXGjBG1Zu3bV9r4vjnxDc5HnwcAtLRpiUkdJ6nWnT4tEoC0odzJyEh81bYZsMoc1LGzs1Nl1Vy4cAGdy3ExMWDAAHSqQGoWEWmfsMQw7Dh+Brd3vY7//U9Est96C8hsbwr/8wqYZQFhFkC6sR66N+oOb1dvDDDvjNY//w7JlWTgQSjQ9JmDenqKYIdEInJbCzbPvXAhvxFb+/bq76TGxvmzLhWXFZMnKUk9qGNpKb43Nwfq1Su878KF4hO9gwMmDhc1xvfuiZiNg7UPoPu6euZPniZNSs55bdSo+HVERFTt9u8XyZp5byO6uqLtWUKCSOR8/XWR2MmER6K6a+5c8bHv6681PZKS6eiIv1teXsDu3cDIkcVvO3HiM59v8wI6jx+LFZcuifSZwMCiJ70oo/ScdKwPXg8AkElkWD94PXSk+WGKU6fyOxhoWq3J1NHX16+UMqnU1FQYGhpCVtTFDxFpl4wMUdCanCxa6bu7IyUrBX+H/w3/ewFo8/0OOMUm4wU5sNagBxYvdsSbb4qeuHfmpOOnleIwwT9/geYTP8pviBwTI6bAAkTkf8YM9fPmBV+USpGtU9pyJgMD8SnbyAho3rzw65kyBRg0SBzD5plmy97ewJMnxf9bPNMA2MGhYI2xFtxCICKiClEoxAXaF1+I3vMzZ4o+9Pb24uItLQ345RdxgbRlCzBsmHju7KzpkRNRdQoKAjZuFPNbWFlpejTP16+f+Pj70Ucikb2kzBf1z7f/MTERH+4B8dk8O7tSxmWoa4jgKcGYtH8S3Bu4w8PRQ7XuwQPRvNnTs1JOVWG1JqhTGXr37o20tDTo6emhf//++OGHH+Dm5qaJoRDVLX//Lfq25OaKkqWCNmwQn1KTk/PnFs8TESH6ugC40r89pvka48yDM5Ar5QCAs2FAl4eAAsDnQYcx+YX8lMmmjTsB2AQA6FDPBSgww1WJsy8BoqxIKhUlSXK5+rqePUX5lYUF4Oqqvk4iAW7eLP7foXfv4tdRsaKiRCsiN7cSmuUREdVgqami2nXPHpEM+tln6kmigEgGnTlT9Abdtk30qujZE/jnHxH8IaLaTy4Hpk4VyeKTJ2t6NKW3eLFIrlm5UvTYKRMDA9HS4PXXgSVLKjVN0drQGnt89kAJpdryU6fEVwZ1SlatQR1DQ0OMHz8evXv3hqmpKS5evIilS5fC09MTwcHBcHR0LHK/rKwsZGVlqb5PSUmpriETaY5SKTJknp3iOShI9JNJShKt9gvOanT1qpiVKCkJGDdO/OUu6O23xVW5uXnhoM6jR+LYec8LkJuaIC+nLjziMk5Gqu+abCABoIQUQF8b9YJiSatWwOjR4pzPdlkzMBDnNDMr+hbHwoWFl+WxtxcPqhYbNuRPcSmVinrriRM1PSoiosoTHi7uXkdGAvv2AUOGlLy9vr74O/jSS+JeQe/eDOwQ1RUbNwIXL4r+NDWp8KR5c9ETbOFCEcB+NmG9kNxcUbuVx9xc3FStAhKJBBKoN/s5dUpcPhQ1F4gmaGtQR/r8TSqPj48PNm3ahDfeeANDhw7FggUL4O/vj4SEBHxdQiHit99+CzMzM9WjuOAPkVZRKERwJSKicHpiRIS4BThzpvjk+KzmzcUf0FatCq/7/XfgvfdEV7Z799TX6eiIoM3jx0B8fOF98zJjkpMLz1f437ocY0P8fddfbZXMyhq7ejhifg9gRxuxzFLRDONaTMP+1/bjxX/uiXNmZ8O18TOt6Xv2BLZvF2VWz85FKJGIFvzNmgHW1oXHS1ohKio/oAOIr1OmiOVERLVBTo64J5KRIVq5PS+gU1DDhiKYo68vAjthYc/fh4hqridPRC+dN97Qjmm2y2rePPER/MsvS9hIqQQWLRJ/1AokV1SWiKQIjNo9CnFpcSVud/w40K1bpZ++3PLabdb4RsmVrVu3bujSpQuOHj1a7DZz587FrFmzVN+npKQwsEPVIzNTTOWUlCRCxM/+3s2eLYIZFhZiRqWC3n5blDMBwOXLQLt2+euio9X/kr7yivq+SqW4ci6qJKmkXjLm5uJhYaHe/DfPe++JDo/m5oBCAYUEuBRzCQGhATiqewinvtRBFtJhrtyJeMUqQCnDvn3AsmUGOJnxGYzaHMGglt64PtobrRpy2o+6IiSkcAxQLhcxRZZhEVFtsGSJeKsOCiq6Fdvz5AV2mLFDVPstXCjiHN99p+mRlI+1tbg3/NFHooSsZcsiNpo1K//aZto0kaJd3JRZZfT46WP039YfdxPu4u/wv3Hw9YPoZF94EqUHD0QRwiefVMppK4VUKgI72papo/GgDgA4Ojrizp07xa7X19eHvr5+NY6IahWlMr9cSU+v8HTSK1YAAQFi/W+/qZf0/P038PLL4vmXX4rQdkFbtoigjrNz4aDO84Ivxa0DRLGrsbEoSVIq1f+IDhokgkvm5oWnsra3z5/1qShjxyIqJQpHQo8gYN9YHAk9goSMhEKbJWUmYfayC9j3UxeEhwPduwN7P5uMwYMn16gUU6ocbm7iTaxgYEcmK1xNR0RUE926Jd7i58wR0+aWV8HATt++YoKYgm/3RFTzhYcDq1aJRuqVMHeQxkybJho8z5lTTDXVmDHAmjXiBnclztCampWKgTsG4m7CXQCAuYE5mlgW/YHy4EHxebN//0o7faUwNGRQp0hhYWGweW5BH9V5588DoaH5/WIKTjf9zz8ijJuUJP46vfmm+r7duoncand3IDhYfd21a/l/zeLj1YM6z2vka24ugjpFBWZatRJ/hczMxKMgJydxTnPzolMdfv+98LI8bduKRzmEJISg6apn5w0vMCwTV5gneOPuIW+svN0KvsNEnKsiH3Kp5nNwEDdopkwRGToyGbB2LbN0iKjmk8vFR4bGjZ9TilBKDRsCR46I5Nxp00QjZSKqPT77TGS6zJyp6ZFUjL6+qK7y8REzkz97zxsdOwKbNokIRlnqUUuQlZuFYf8bhgvRFwAA9ib2ODL2CMwNzIvc/sABUd6mbcHxOhXUiYmJQXJyMlxdXaGrqwsAePz4caHgzcGDB3Hx4kVMnz69qoZCmqBUimJDIyP1LJO7d8WnnaQkEfAoGC3IzBSfgpKSRPDl8GH1Yy5eDOzaJZ4PHKgeNc7IAM6eFc+fbfQhkYigSnx80cGXkgI3DRsCr70mtimq7fqePaKPTVF/bcaNE4+iGBmJ11AFlEolrsZdRUBoABoYN8DYdvkNkZtYNkFDk4Z4mPoQAGCqb4q+jfvCTeqN6/u84L/TFckmwPQpwLQ/edFO+SZOFP9l790TGTr83SCi2mD5clFydfJkydP7loWTk2gjN2aMaLzs61s5xyUizbp4EdixQ9zoMjLS9GgqbsQIkfT/yZwcnPpgDyS+PurXbaNGVdq55Ao5xu4di2PhxwAAFgYWCBgTACfzots5ZGYCx44VLpLQBrUmqLNq1SokJSUhOjoaALB//35E/Xch/d5778HMzAxz587F5s2bER4eDmdnZwCAp6cn3N3d0alTJ5iZmSE4OBgbN26Eo6MjPtGmYjkSdRapqSIIYmEBmJrmr3v6FPjhB7HO1VUUYxb06qvAn3+K21/Jyer7nj8vbl0BgImJelBHX190F8zNLTT7EgD1bJekJPWgTl5QxcREBJSeNW2aOK6dXeF1n3wCfPihOL6envo6Jyfx17s4rVsXv64axabF4mjYUfiH+uNI6BHEPRVNxzwcPNSCOhKJBFM7T0VGbgb6OvdHzPkuWPGjDvaeERfqy5eLOJSxsaZeCWkzBwcGc4io9ggJAT79FJgxo/Knyx09Gti/X7TX69ZNfaJKIqp5lEpxudCihZh8tjaQSIAfZ94HRr8GyWtngIz0KnlxuYpcvPPXO9h1U9ycr6dTD3+N/gut6hcxIcx/AgNF4CSvC4Y2qTVBnSVLluD+/fuq7/fs2YM9e/YAAMaMGQOzZ0tN/uPr64sDBw4gICAA6enpsLOzw6RJkzBv3jzY2tqWZyhUktxcMS9nUpL47Xu289/SpcCVK0BKisg4KRiZ/fnn/ODLli2Fp7/OC5v26VM4qCOTiYAOIM5dMKhTUi8ZiUQEUYoLvowcKTp5mZsXnsq6SxexX3ENX0oK8xbVULgYUVHiQ6Cbm2YvbjNzM3Ey8iQCQgMQEBqAK3FXitwu6GEQkjKT1NIap7abiw0bgHETgPv3Rbrlvn3ijyb75RARUV2gVAJvvSU+TixcWPnHl0hEv4o2bcS0wf7+ojcZEdVM/v6i1ea+feozfNd0nrIgAGcAAMqZMyF59dXCbSMqIDEjEb67fXEk7AgAQEeqg999foenY8mR9L/+EpeFRTZx1jAjo1oy+1VERMRzt/Hz84Ofn5/asoULF2JhVbxz1nbXrgGxseK3Z+hQ9XX79gHr14sAyYIF6gWRcXEikwYAhg0TgZuCDh0C8mYdS0sTWS55ns2KKcjQMD9wU1Q5U9OmonzKwqJw1oy7O7B5swjMFPW/9Nkpugvy9haPolRDNGLDhvxpnaVSkXo5cWKVn7ZI++/sh89unyLXGekaoXfj3ujv2h/ert4w0xc/y/Bw0RN6wwZRrfbaa8D77wMdOhR5GCIiolrL319MlevvX3VlFJaWgJ+f+OiycqXICCKimkcuF1k63buLkspaxccH8TuPInXvEVx5ZyeGVmJABwAOhhxUBXR0pbrYMmwLBrgNKHEfpVL00xk0qNIm3KpUtSZTh8rh9m2RFZOUJHqpFJwa+/59cYWdt27JEvV9J0wQRZxSqchGKfjb/eCBCGXmPS+oNE1+8yQlqQd1nJ3FpxAzM9E9sCCJRPxPMzYGimpw/fXX4lEUe3vgjTeKXqfFoqLyAzqA+DpliugxUlUZO4+fPsbRsKMICAvAyJYjMdAtvwdPX5e+kEACJZSQQIKO9h3h7eINb1dveDh6QE8mysiUSuDMGZGUtXev+JFPmyaSq5gKTkREddWyZaIPqJdX1Z7HywuYPl1MHezlpZ13nYmoZNu2iXvsZ85oZ5ChTG7fLlS9Yb3tR8wdl40Dm83h/bkIWlSW19u+jrNRZ7Hzxk7s8dmD7k7dn7vPrVtARIR2ll4BDOrUHEql+Enp6QH/NXkGIGY52rVLBEDathXhw4L69xeBG5kMePhQfd3u3cDnn4vnf/6pHtSRycRfCUAUaj4rL/iiUBTOqCkYmElJUd/P0FAEi8zMxExMz/ruO2D+fHGM+vXV13XrJm5fFUfb5parYiEh6tM5AyJqf+9e5QV1suXZOP3gNPzv+SMgLADBMfmzdOlIdNSCOpb1LPFVr6/gZuWGfi79YG1orXas3FwxgdbSpcC5cyJ56qefRDytMv9QExER1TQ3bgABAeJCrTou0BYtEnM/TJsmGn/W+ItCojokM1Ncwg0fDnTtqunRVEBCgkjR37EDOHVK/cUYGmLud4bY3Fxk9X/8cflPk5SZBCNdI+jK8q+hl720DB93+xgNTUt3R/nAATHJcaEZubSEoWHRxSqaxKDOs774AvjmG3HFfvy4yLPLExeX3z9m4sTCQZ2EBLGNTCYCQwXftUvKmslbZ2xcdJHmqFHiP565eeGC7GHDRGmWubloNFyQRFJyk18Xl+LXkRo3N/FPXzCwI5OJ5sLlpVQqEZYYhgMhBxAQGoDAiEA8zSm6QPNE5IlCyz7v+XmhZUlJohpvxQqRuNWnj2jUOHAga/mJiIgA4McfReLwyJHVc7569cRNlkGDxH29V16pnvMSUcWtWQNERxdfhFBj/PabiGQDogrk8mW1a0cXF9HYfdEiYNIkwMqqbIdXKBXYcmULPjr6EeZ2m4v3u76vWqcj1Sl1QAcQQZ2+fcXfTm3ETJ2aQE8vv8lvccEXoOjwnIODmDbbzEyEdQv+JnbvLmaMMjcvHOY1MgJycorvuvXWW8WP18iodsypp+UcHEQPnSlTxK+HTAasXVvxLJ0tV7Zg/vH5Ra5rZ9tO1RfnxUYvlnic0FARyNm4EcjKErNuzJwpZognIiIi4fFjYOtWMX/CsxNeVqWBA0X51ezZwIAB1Xtu0qxHj8RlQ1ZW/sPMTJTi8YabdktNFff6x48HmjUrfrtseTYepjzE05ynyJZnIys3C1nyLOQqciGTyKAr04WOVAe6Ul20tGmJeroaiFZMngz88ouYafjjj4v8I/TZZ8CmTSKAtXRp6Q6rVCpxNuosPgj4AGeiROXJvMB5GNV6FBoYNyjzMJOSgJMngVWryrxrtWFQpyZwchKdY83N1cucANE/xs+v6D4zAPDHH8Uft1274q+wJZLa1Ua9lpo4UVSd3bsnMnRKE9DJkefgbNRZMUtVWAA2vbIJLW1EQb1EIoG3q7cqqNPAuAG8Xb3h7eKNfi79YGtc8oxwSqX4o7dsmfjVs7QUWZXvvlv05GFERER13Zo14kJ6ypTqPa9EIu7ttW8vZsV6//3qPT9VH7lclL7v3y8e168XvZ25OfDii6LjQY8e4p4vgzzaZflyICktE69OvYXdN0MRnhiO8KRwvN3pbbS1bava7mzUWfT061mqY4a8F4Imlvmp/r9e+xULji+ArbEtbI3Ew97EHo3MGsHRzBGNzBrB3sQeOtIyXCsmJIgmQAXrl2QykalT1CzC/6lfX8R7vvpKXE+UVJGQlp2GHdd2YM2FNbgUe0ltnberN5TPTpZTSgEB4v/QwIHP31ZTas3sV7Xa2LGFp+/Oo68PjBtXveMhreLgUHIwR6lUIjQxFAGhAfAP9cc/4f8gNTtVtT4gNEAV1AGALg5d8IP3D+jn0g9t6reBpBSF9jk5orXTsmXAhQui19maNeLXVlvTFImIiDQtK0v0lxs/XtwIqW5t2ojk66++Eu/ZZS1vIO2WnCyyOjZtEhlhVlbiwvTTT8XNNn19wMBAfI2LA06cEI+FC8UFYsuWYltf32qZ1JWKkJqViqCHQTj/8DwuPLiKPx5dgfLDu3j5T7nadl0adlEL6jzb27IkebPS5olIisCt+Fu4FX+r2H1kEhleaPgCTk88rbY89EkojPWMUd+ofv41xI8/Al9+KXpG3L6tHsApRaf2mTPFdcXHH4uWsM86GHIQ++/sx/Zr29WucQCgmVUzrBywEl6u5e9A/9df4m9lo0blPkSVY6YOUS11KOQQ9t3Zh4DQAIQnhRe73Z34O2rf60h1MMtjVqnOkZgoSsBWrhR9uL28gIMHRfYQ7+wQERGV7NdfxcW0JqcWnz9fjGP+fJEFQDVfbq7oZ/jFFyI48847ouVl167FB2datMhPosjNFX1rv/sOeP11cT3+ySfiecH5WqhqKZVKNF7eGAkZCfkLiwm8PvtZv4FxA/i08oGJngn0ZfrQk+lBX0cfulJd5CpykaPIEV/lOTAzUA/q5ChyYKhriPSc4qMEcqUcMmnhX6bx+8bjZORJGOoaorF5YziZO2H2sUj0/q+FSOz0N5G05kc4mDrAWM+4VP8OhobA118rMW5qHDYeisGbA9zV1n8Z+CXOR59XW9bZvjPe7vQ2xrQdo5qBtzzkcuDQIdHTR5tpY1BHoixvbpQGpaSkwMzMDMnJyTA1NdX0cKiOUSqVhTJqRvw2Ar/f+r3QtjaGNvBy9UJ/1/7o59IP9iZFpzuWJCREfPDbtEm88Y8ZI9K227Qp7ysgIqo+fM/WHnX5Z6FUitInR0dxJ1iTvvtO9K64fr3kPh2k/Y4eFZkN16+LZP6vvwYalr4fbCEXLohj/PGHmLl02zagc+dKG26dp1AqcDn2Mg6FHEK2PBtf9f5Kbf3A7QNx6N4h1fcypR7a2LVEW9u2aGbVDI3NG6OxRWM0tWoKy3qVm+6Xlp2GuLQ4xKbF4mHqQ0QmR+JB8gNEpkQiMjkSHg4eWDVQvdGM45KGeJgaDWWBm7vWT4G7K4E9LYAvegPRpsCy/svUGheHJ4Zj7N6xMNQ1hEwqQ2ZuJjJzM5GRk4H0nHREpUQhIzcDErkBcualQybLv+756MhH+P709zDUNcTo1qPxdqe30dG+Y6X8G5w+LUoST54UX7XVmjViNsOcnKqdzbAs79nM1CEqhYikCNVU42cenEHo9FC1Jmfert74/dbv0JXq4sVGL6oaHLdv0B5SSdnTaJRKMfnasmVipgxra2DOHHH3x7bkVjtERET0jH/+Aa5eLX3zz6o0Y4a4KJg9W/RcoZpHLhfZNN9/L3rinD8PdOpU8eN26gTs3QtcuSJK9Tw9RVbXhx+yJKu8lEolgh4G4bcbv2HXzV2ISokCAJjqm+KzHp+pTb09ouUIOJk5IeQfT5z/0x33zjaDjVX1pEsZ6xnD2NIYrpauz99YoYByzx6c/SkLWwa1x5bmmQhPDEeWPAvxRoDLDCCpQEuG+kb11XZPzEzEqQennnsapSwTa3fE4t2x+c06x7UfBw9HD/Rp3Aem+pV7c2DdOlF2pe1Txxsair8BOTna0/SeQR2iIqRmpSIwIhD+of4ICA1AyJMQtfUnI0+q1Yu+0uwVOJg6oKdTTxjplX82suxsMePg0qXApUui9PWXX0QKroFBuQ9LRERUp61ZA7RuDfTpo+mRiPfzb78FXntN3Jn29NT0iKgsUlLELKOHDgFLlgCzZlX+3fp27cTvxrx5os/O4cNi1jZt7jOiba7FXcPmK5ux6+YuRCZHFlqfkpWCC9EX4OHooVr2pvub6GX2JpoNE72ObLS179XJk5CMHImGAOb+bYu5y29AIQEeP32MyGSR2XM/+T6iU6Px6OkjtLBuobZ7caVe+jJ9GOgYwM7EDo3NG+PWmcb4Zifw5sj865CWNi3V+oNWlkePRGnqggXaH8A0NBRfnz5lUIdI68gVciw6uQgBYQE4/eA0chW5RW5nWc8SsWmxastsjW0x0K38bdqfPBFTpK9aBURHiz45/v6ib05VpvURERHVdk+fipKrefO05z3Vx0dcNH71lXi/p5ohLAwYPBiIihK/UwMGVN25dHVF4+X+/UVj7bZtgR07tHtWIG0QnRqNoTuHFur7AgC6Ul30c+mHwU0H46UmL6GxReHZjL/8UmTIT5tWDYMtr+7dRTT49GkxW/OjR5A2aCBm0DK2ReeGJdfsdWvUDdmfZSMjNwO5ilzU06kHfR39QtUFdzoBrdeKNhAffVSVL0jcxJbJRIaatjP67/59ejpgYaHZseRhUIfqrLTsNLWmYTKpDFuvbsWdhMLNjD0dPeHt4g0vVy90tOtYZLOy8rh7VzSp9/MTTerHjhX9clq1qpTDExER1XkHDwIZGcDIkZoeST6pVASZfHyYrVNTHD8uGiBbWgJnz4pmx9WhZ09RjvXGG8CQIcCGDZyMtyS2RrZqN191pDrwcvGCTysfvNLsFVjUK/4q/MYN0cdo1ar8C3eNunED+Pln0VRzzZr85RIJ8MMPQFKSiPqVI1qtK9NVKz0rSrNmwNtviz5Po0eLnmRVISdHvMzXX9fMzIRllZepo03NkhnUoTrjafZT/Hv/XwSEBiAgNAAyqQzX3rmmto23qzfuJNyBm6UbvF294eXihd6Ne1dqzahSKWr7ly0Td3nq1xfTBr79tnhORERElee334COHQEXF02PRN3w4eImDrN1tN/p0yIrp0sXMc1zdV94WliIXjvvvAOMHy9mcZszR3syzzQhR56D32/9jmtx1/B1369Vy2VSGSa0n4D9d/djovtEjGo9ClaGpauj+uwzwNlZS7JF5HKRsh8TI1JYPvlEvf6umhrPLFgA/P478O67os9nVfzO7d0rZvZ9773KP3ZVYFCHqBrldbjPC+KcjDyJHEWO2jbRqdFqM1J94PEBZnadWWQ6ZkVlZQE7d4pgzpUrYvaqTZtETb2+fqWfjoiIqM57+hQ4cEBkxWgbZuvUDJcuiZKnzp3F71K9es/fpyro6IhGsg0aiFKYmBiRrCEt+3wcNVpKVgrWXFiD5UHLEZ0aDQkkmNhhIlws8qO2n/f8vNDMVs9z7pyYdWzLFg31SYmOBuwLzJKbV4u0YIEY0PnzGmmqZG4OrF4tstT+9z9g1KjKP8fKlSIjrW3byj92VWBQh6gaRKdGY86ROTgSegSP0x8XuY1MIkMXhy54/PSxWlDHydyp0scTH5/fLyc2VnwwWLIE6Nu3bt9hIdJ2UVFASAjg5gY4OGh6NERUHgcOaF/pVUHM1tFut28D3t5ievE//9RcQCePRCKu8Rs0EFkNjx8Dmzdrf2PZyvDo6SOsCFqBn87/hKTMJNVyJZT43/X/YW73uaplOtKyX+J++qn4vzh6dGWMtgzWrRNRk/BwcaFQ8Jds8mTR4OeNN0R0RUOGDgVGjACmTwf69RNDqiyXLokpzH//vfKOWdUY1CGqZBk5GUjMTFQLzJgbmGP3zd3IlmerbdvYvLFqqvHejXvD3MC8Ssd265bol7Nli/h+3DgxjWl11WATUflt2CA+SykU4i7ounXAxImaHhURldWuXdpZepWH2TraKyJCXMDa2oqZrkwrd/bmCpk6FbCxEQEIPT1g/fram7ETnRqNRScXYX3wemTkZqiWSyDBkGZDMKPLDPRy7lWhc/z9N3D0qCgDqvYA2ZkzIoUfEKlCr72Wv87BQURStMDKlWJW3pkzxUxslXlcR0fRL6qmKDj7lbZgUIdqFKVSieuPrqumGj9+/ziGNh+KnSN2qrYx1DVE90bdce7hOfRp3Afert7wdvVGE8sm1TA+4NgxMSX5oUPiTspnnwFTplRuVJuIqk5UVH5ABxBfp0wRvQiZsUNUc2hz6VVBzNbRPo8eiYCOgQFw5AhgpYVTW/v4ANnZIonDxETcSKxJGeClyYZdfGoxPv/nc2TJs1TLdKW6GNt2LOa8OAfNrZtXeBxKpWhX88ILwCuvVPhwRXvwQKR6nTghpjArGIEbP17MmNKli3ZFDp/RoIG4vpkwQTQ0fumlih/z8WPxz/Hll6K8sKYoOPuVtqhB/3xUV8WlxeFo2FEEhAXgSOgRxKTFqK0/EnYEcoVcbUaqLcO2wMbQ5rld3StLZibw66+iX861a0C7diId1teX/XKIapqQkPyATh65HLh3j0EdKr+0tDQsXrwYQUFBOHfuHBITE7Fp0yaMHz/+ufv6+flhwoQJRa6LiYlBgwYNKnm0tYO2l17lYbaOdsnJET+LtDQxy5WdnaZHVLwxY8Q433lHxAMWLND0iEqntNmwtsa2qoCOoa4hJneYjFkes+BoVnnTMP35JxAUJDJ1qiwoNnNmfn3R1KliSvI83buLOr9mzaro5JVn3DgRhJkyBbh+XQQTK2L9evFvrhWNqcsgr0KOQR2iUjh+/zhmHJ6By7GXi93G0dQR3q7eSMtOg5mBmWp5wXKsqvT4sZiC76efxF2dQYPEnZLevWvW3RIiyufmJj5kFgzsyGRAk6pP9qNaLD4+HvPnz0ejRo3Qrl07BAYGlvkY8+fPR+PG6o38zTXYZ0HbabL0KjYtFpdjL6se1x5dw9ZhW9HBroNqm7NRZzFoxyDYm9ijgbEdzMfZY8LGpvjZrgs623eGiX4Fr5ioXD76CDh1SpTkODtrejTP9/bbQGoq8OGH4iL7ww81PaKSFZcN2633U5jXT4Otsa1q29FtRmPZ2WXo17gf5rw4B/WNKneaWLlcZNT36SN6XVZIXJxI6zp3Dli+XP1C4JVX8oM6hw+rB3Wk0hoR0AHES1q3DmjdWmTs/O9/5S9Xi4wUjb5ff73mVTPIZOKmPYM6RAUolUrcir8FcwNztWCMhYFFoYCOka4Rejn3UvXGaWrVFBINRE9u3BDBm61bxd/i8eNFv5wa8jeZiErg4CA+tEyZIj7wyWSi2TmzdKgi7OzsVFk1Fy5cQOfOnct8jAEDBqBTp05VMLraJ6/06ssvq++c4Ynh2HFtB7Zf245b8bcKrU/JSlH7Pjo1GgkZCUjISMC1R9eAxkASgL5bRL+QljYt0dWhKwa6DcSgpoOgJ9PElDx1y44dIut6xQr1625tN2eOCOx89JGYbl2bMx+Ky4b1WDwW3n311Foq6Eh1cGHSBbVs/Mq0davIOAkKqoSDTZggei8AIhun4EXByy+L+spXXqk5UzwVw9lZ/D8ZNkxc+6xcWfYb2VlZovGysTHw3XdVMswqZ2jIoA4REtITcDTsqKo3zsPUh1jQewE+6/GZapvW9Vv/d/eqgSqI4+HgAX0dzdQzKZUiAL90qah5t7cX6dKTJ2tnrTURld/EiaKHzr17IkOHAR2qKH19/Uopk0pNTYWhoSFkdWG6mwqortKrjJwMbLmyBVuvbsWpB6eK3U5PpgelUqm2TK6Qw8nMCTFpMYUmd1BCiRuPb+DG4xvYfm074ufEM6hTxa5cEcGQsWOBadM0PZqy++orMePq228DTk6Al5emR1S0orJhIclFYr1z+N+Nh5jjOQcd7TuqVlVVQCcjQ2Tp+PiIfjqlsmsXEBgo5pPfs0d9XZ8++UGdw4fVgzqWlsAXX1TGsLXCkCHAmjXiGsjeXvQkKosZM4CrV8WsVzX1GopBHaqTsuXZOBt1Fv73/BEQFoCL0RehhPqHG/9Qf7WgjkQiQch7ITDUNazu4arJzAS2bROZOTduAB06iMi+j4+YcYCIaicHBwZzSLv07t0baWlp0NPTQ//+/fHDDz/Azc1N08PSSnmlV89Uq1W6zNxMzD02F4mZiWrLPR094eHggfYN2qN9g/ZoZtWsUJ+/ka1GYmSrkVAqlUjMTERUShS+33oJO/4NQivvINxOuopcRS76u/aHkZ6R2r5br2yFg6kDejn30kjGcm3z5InIPGjWTFys1sR/UolEZBhFRIgsiFOnRJmMtnFwAD77/j7mf+gAKGSAJBcYPAUwe4gRLUfAop5FtYxj+XLROuGbb4pYmZYmeiw8+wdkyRJRXgWICFrBuqGBA4GEBNFB2MNDbbfSNIWuaSZNErGtTz8VfaeKaftWyKZNIvt5/XqgJieeGhpy9iuqY5afXY7P/vkMadlpRa430DFAT6eeGOg2sNA6TQZ04uKA1atFz5z4eBGV/uknoEePmvlmT0RENZOhoSHGjx+P3r17w9TUFBcvXsTSpUvh6emJ4OBgODoW3TQ0KysLWVn5s8akpKQUuV1tU52lVxb1LPBZj8/wQcAHaGnTEmPajMHoNqPhZO5U6mNIJBJY1rOEZT1LrHu3Lfy/G4eeTsC5ZRn49/6/MNVXnxEnMzcTMw7PQGJmItws3TCpwyS86f4mrAxr6C1vDVMqRQPY5GTRR8dQs/cSK0RHR/Q56dZNVPwEBYlZi7RFTGoMPv37U/il+QEz7IEnTQDLe+jexgWLvc6ii0OXahlHfDzw7beiwbSra4EVmZkiGnz7NvDii8Dx4+o7dusmgjpSqZgZpXfv/HUtW4qDPqO0TaFros8/B6KjRYCnfn3xO1eS4GDxb/7WWzX/38DISLsydSTKZ3NBa4CUlBSYmZkhOTkZplo89Vtdk5iRiGPhx+Dl4qXWtPjXa79i9J7Ratu2s20HLxcv9G/SH90adYOBjkF1D7dY166Jeurt28Wb45tvijRBNkklIio7vmcXltdTp7SzXxXl5MmT6NGjByZPnow1a9YUuc2XX36Jr776qtDy2v6zOHBATFxw5w7QtGnlHffx08eYdmgaVry0Qq2Za1ZuFm48vgH3Bu6VkjWzYIHIHrh/X1woPWvHtR14fc/rasuMdI0wqcOkSp8ZqC74+Wfg3XeBv/56/kVpTREVJWbItrcH/v1XOwJV64PXY6b/TLWbvK4WrljstRhDmw+tvoyzyEgEjFgL+aWr6Pb1QJh8+I76ehcXIDxc/KMlJ6vPtX3zJvDwIdC1a6mmfoqKEqVwz068EBFRezJ25HKRGfbnn8Do0aIUq0UL9W2USlFuNXSoKLc6eRIw0J5Lv3J58UXx/rJpU9Wdoyyfn6RVNwyq7XIVuTgVeQrz/pkHjw0esF5sjZG7RuJY+DG17fq59IOdsR3GtB2DrcO2IuaDGFx++zIWey9GP5d+WhHQUShEGayXl+hfduSI+FAVFSUagDGgQ0RE2qRbt27o0qULjh49Wuw2c+fORXJysurx4MGDahyh5hw5Ii6kKrMy7VTkKbivdcdvN37DV/+qB8r0dfTRwa5DpV2UTp0qLvxWrCh6/fAWw7Fz+E70adxHtexpzlP8GPQjXFa4YPwf43Hz8c1KGUttd+cO8MEHInugtgR0ABEw2L8fuHVLzC70bGNiTTDWM1YFdMz0zbDEawluvHsDw1oMq/yATni46J3w+eei7qmAB7fS4H3+GwzI/QsmFwML79u1K+DuLporpT1TZdCypbhYKOVc3sU1hb53rwyvRcvJZCI7bMUKEUBs1Ur0Mrt8WWSKffSR+Fvcvr1okLx7d80P6ADsqUM1XFhiGAJCA+Af6o+/w/8uNJMDAASEBuDVFq+qvrcxssHDWQ+1suY7I0P0x1m2TGRaduokOrqPGAHo6j5/fyIiIk1xdHTEnTt3il2vr68PfX3NTC6gSUeOiOuuyvjYoVQqsezsMnx09CPkKnIBAH/e+RNL+y+tsptSlpZi9r2ffhIXRM9eP+rr6MO3tS98W/siJCEEK4JWYMOlDcjIzUCuIhebr2zGlitbMNF9In4Z8kuVjLGstLGnSE6OCHg0aiRapdQ2HToAO3eK9gFffAEsXFi951cqlWqf/X1b+WLtxbVoZtUMC3ovgI2RTcVOcOuWmLoqIgKYPVv9P/zevSJaB4hfugIR3o83NsVG6EEf2UBoaOHjbt9eaX0WimoKLZPVvpvFenoiGD1pErB5s6hCc3cX62xsRIbOqlWil3Rt6UfKoA7VWMN/G449t/YUu76VTSt4u3pjaPOhhdZpW0AnJia/X86TJ+KPzbp1olRWy4ZKRERUpLCwMNjYVPDCqJaJihIVEvPmVfxYyZnJmLBvAvbe3qta1tOpJ34d/muVZxnPnCkyhdety782LYqblRtWDlyJL3p+gZXnVmLVuVVIzEyEEkpY1rOs0jGWlrb2FPnqKzHj1Zkz2lGeVBUGDQIWLRLBwTZtAF/fqj9nRk4GFp1chLCkMGwdtlW1XCKRIGBMQKGG4cXKyQGOHRNBG2NjYMwY9fXvvw8EBIjnEyaoNy0uOPPUrVuqp+fPAzt+08Hrcw5j4LuNRUrfsyrxQsDBQfy+T5kiMnRkMtEkWFsCm5VNT08EdiZMEJliFhZA9+7iddc2hoai/6q2YFCH1MgVclyIvoCTkScxy2OWWjCmhbV6gaS1oTW8XLzg5eIFb1dvNDRtWN3DLbMrV0RWzo4dgL6+6JczffozTdKIiIg0JCYmBsnJyXB1dYXufymjjx8/LhS8OXjwIC5evIjp06drYpha6+hRcU3Wt2/FjpOYkQivrV64GHNRtezjFz/Ggj4LoCOt+o/PDg6i+mPpUjG99vMSrmyMbDC/93x8+OKHWHNhDX6+8DM+fPFDtW0ycjKQmZtZbbMLASLIlhfQAcTXKVOA/v01e2F76pTIJvjqq5o9A09pzJkj+plMmCAyRDp2fP4+5bX/zn5MPzwdEUkRAIA32r4BL9f8udULBXTu3QP++AOIjAReeUX9P65SKWriFArxQ3o2qOPion6cgkGdjh2BxYtFcOe/lBGFQvTIbNMG6P9tb6CaAg0TJ4rf93v3xL9/bQ3oFKSjI2aTq82YqUNa537SfRwJOwL/UH8cCzummpZzUNNBaGadH+ke0GQATj04JRocu/aHu507pBLtb8ukUAAHD4pgzt9/A46OogHhW28B5uaaHh0REdUVq1atQlJSEqKjowEA+/fvR1RUFADgvffeg5mZGebOnYvNmzcjPDwczs7OAABPT0+4u7ujU6dOMDMzQ3BwMDZu3AhHR0d88sknmno5WunIEVF2YlWBiaCeDehYGFhgy7AtGNR0UCWNsnTmzAE2bhTVIG++Wbp9jPWMMdtzNmZ2nQmZVP2qdemZpVh6dim+6PEF3un8DvRkVV8HUVJPEU1d3KamioBZ167Axx9rZgzVSSIBfvlF9A8aOlRkq1T2jFjhieGYcfA9/HPjANL+C0DqSHVw4/ENeB0JFR/Eo6LE14Inv3NH/KIDou6wYFBHT090eo6KEl3Dn/XKK+JDfZMmheuZGjQQJVkFbN0qsrICA6s/c8TBoW4Ec+oSbZv9ikGdOigtOw2BEYEICA1AQGgA7iQUXY8fEBqgFtR5sdGL+GfcP9U1zAp7+hTYsgX48Ufg7l0xC8DOncDw4eqN7ImIiKrDkiVLcL/AxcmePXuwZ48oax4zZgzMzMyK3M/X1xcHDhxAQEAA0tPTYWdnh0mTJmHevHmwtbUtcp+6SKEQmToVKe1JzEhEv639EBwTDACob1Qf/4z7By1tWlbSKEuveXNx3bp4MTB+vChdKq1nAzqxabFYdGoR0rLT8L7/+/jp/E/43ut7vNLslSotkdfGniIffww8eiR+V+rK58F69URCTKdOwKuvAv/88/zsLzWpqcCNG0BsrPjhtW4NQMz8tuTUYowe9gV+T1biWn2g49tAn8Z9sHLASvH/ZvM7ohYHAB48UA/qFIx0REYWPu/nn4tfIGdnkblT8Hf1pZfEoxSSk0UJmq8v0LNnGV43UTGYqUMapVAq4PyjMxIyEopcb2FggX4u/eDt6o2BbgOreXSVIzpaNONauxZIShJvXps2AZ6emh4ZERHVZREREc/dxs/PD35+fmrLFi5ciIXV3eW0Brr6//buPCyqsv0D+HdAVmVRQBZBVMAVF1xQyj233NJErbS019Le0lxS0ywt37efmVuWu7nla1lSVlpukZgrokhmLuAKyK4IKIs4c35/PDEwsjjAzJwZ5vu5Lq4ZzjLnnuOReeY+z3M/58WX9b59n7xteawtreFoI6aOda/tjt/H/S5LQqfI7Nli6ty9e0XB2+oY3nw4tp8XNU7i7sZh+LfD0adJH6wcsFJv79HYaoocPSpqKn7+ueboHXPQoIFI7PToAUyeVIgNW6w0y8f88IPI9qSmAitXAp6exetOnChOoLz/PhAYiPDr4Xjz1zcReycWrygBKxXg/cAC34zYgdGtRhcnCxv8U57B0hLIyNAMyt9f3HH19i470zdxok7e+8KFIi9VEwtikzyMLamjkCRJkjuIyqrMnO3mKjE7EYeuHUJ6bnqpMdXDdg7DT1d+AgBYKiwR4hOCfk36ob9/f3Tw7FDq7o6piI4WQ6y+/VZMlffaa8CUKUDjxnJHRkRkvviZbTxq+r/FkiXAhx+KCRCqM+nXg4cPMOHnCZjfY76sCZ0iXbuKx2PHqv9aZ5LO4J2D7+CPW3+ol1kqLDEleAo+7PkhnGzL7i1WXYmJ8tcUyc8H2rYVQ/OOHq2ZxVuRlVWc3QwIANq00Vzfti0ext5AbH5DhH92AVOnllg3ebKYcg0ATp0SXdyL/PmnmJMaACZMwMkFE/DU5uK7pb/sANqo3ODetD2sftmn2aPmzh3g4UOgfn1ZTvrFi+LffeFCYO5cgx+eaqilS8WMcvfu6e8YlfnMZk+dGiK3MBdHbh4RQ6quH8TF9IsAAHsre0ztPBU2tYpbNy8EvgAvBy/08+uHXo166e0D3BBUKnH3avly4MgRUcR+8WIx9rycXuxERERUAx06BHTvXr2EDgDUtq6NnaE7dROUDsyeLYZhHT8ueu1UR0evjogYF4Hdl3fjnYPv4Oa9m1BKSnwW+Rm+vvA1lvZdipfbvqybwEswhpoiCxeKiZR27zaRhI5SCdy/X7pB+8034mJPTS3d7Skqqrir2ty5pZM6WVmwzs+Br10a3nkHaNUK6NPnn3Ulh0U9Pq2Pj49I+nh6Ap06oYt3F/T3648D1w4gxDsEDf5YC2+PtmW/j+oUuKomSRITojRqBMyYIVsYVAPZ24tSH8aCSR0TpZJUOJ96Xl0X52j8UTxUPiy1XW5hLo4nHEfvxr3Vy14IfAEvBL5gyHB17v59YOtW0Tv06lUxtGrXLlEAzlzGRxMREZGQlyd6X3z8ceX3DbsYhj5N+sDZ1lnncenC4MGivs6SJdVP6gBiaunnWzyPZ/2fxZITS7Do2CLkP8pH2oM0XMq49OQXMEHnzgGffiqmum8pf+cr0Z0sKkokT1q2LD0Fl5+fyEA1by5q2ZR08qSoKwCIGjUlkzola2yVNd9yQABgY4Panl54xlrCqFEKREX9Mwvsyy+LDI+7e/GQqX/EShlo+sUX6t8VAFYNXIWImxH4V9C/jHbilO+/F7Oi//JL9ZO9RCXZ2wOPHgGFhYCV1ZO31zd+/TVRt+7dQtD6oDLXWSgsENwgGP39+qOfXz8ENwg2cHT6k5hYXC8nJwcIDQX+9z/NHqJERERkXo4dE8Nr+vWr3H77r+7HqF2j4FfPDz+O/hGt6rfST4DVYGEhJgiaMAG4fFl8z9cFOys7zO8xH+PajsPMQzNx+vZpzOs2TzcvbkQePRLnrmVLUSxX5yRJ3G10cNBcvmMHcOCAKC785ZdAw4bF686dK65R8+67pZM6KpX4KSsxUzJxk5amuc7bG5g+XWxT1tzlhw4BACwA7MwU7efnnhN5IgdfX9HlvYTMvEy8F/4e1p9dj59f/FljBjj/ev7wrydjxesnyMkRvXOGDAEGmmaZUDJitWuLx9xc4xgdwqSOEct/lI9j8cdw8NpB+Dj6YErnKep1jes2hn89f1y9exUA4Ovkq07i9G7cG3Xt6soVtl6cOSPq5Xz3nciMTpwo6uWU/HwkIiIi83TokBgZ0qoSOZmrd6/ixe9fhAQJV+9exc9XfjbKpA4AjBkj6tMuXSryA7rk6+yLXSN34W7eXdS2rq2x7vPIz+Fs64yX27ys11my9GnZMlES5tQpMUt2pdy/L4qyJCeLIo2PD2dq3hy4fl0UC7p4UXNdVJSYRxsQPWpKNlqf1KOmqPiPp6dI7pSc+uzVV0Umpn790kOb6tYVNQm0ULcu8NNPIrEzdqwYllZ0GEmSsO3PbZh9aDbSc9MBAFP2TUHvxr1hb2Wv1evLbfZs0SFq5Uq5I6GayP6f/wZM6lApkiTh7/S/1UOqjtw6gvxH+QCAtu5tNZI6ADAzZCYKVYXo79cf/vX8TfbDtjxKJfDzzyKZc/So+CxdulTUy3n8ZggRERGZr0OHxMgRbZtC9x/ex/Bvh+Ne/j0AwLDmw/BuV31049ANGxtg2jQxw/PChYCXl+6PUc+unsbv1zOv493f3kX+o3xsOrcJqweuRmD9QN0fWI+uXxfFs6dNAzp1KmejgwdFYcakJFH5tOTQo9OngWeeEc9nzy6d1CksFD8pKaVf90k1aubNE8mdogLEJf34Y/lvystLZxdAixaiRM+QIWKq908/Bc6nnsdbv76FY/HFlbnrWNfBlOApsLIwgnEmWggPB9atE3WfOWGK4SUmAnFxYrSf3LW09KVkUscYMKkjs7t5d3Hg6gEcvC4SOUk5SWVu91faX7iTewcu9sUZ+UkdJxkqTIPKyRFDhVeuFB/GXbuKWRaHDjWRwnZERERkMGlpQExM5QqhvvnLm7iQdgEA0Ny1ObYN22a0dUGKTJokcg6ffSa+fOtb2MUw9c3FP279gXbr2mFal2lY0GMBHGyM8O6aSiWmzLawAFxdIUnAW28Bbm7A4muhQLur4q7g0aOa++3fL+4gAuLOYcmkTslpvZOTSx8zMFC8pqdn8ZztRcaPF8Ue3d0BZ2fN/ZycxD+mERg0SHTumT4nC9H1FyAidxWUklK9fmTLkVjefzm8HU3j23lOjhhu17Mn8MYbckdjfjZtEiMqijqYbdgg/j1qGiZ1SMOJhBN46YeXylzn7eiNfk36oa9fX/Rp0kcjoVMTxccDX3wBbNwoeruOGgXs3FnBnRUiIiIye+Hh4lE9i88T/BL7C7afF8NiHG0c8ePoH+FoY/xTvDs5Af/+N7B2LfDee6XzBLo2++nZaOPeBpN/nYxrmdeglJRYdnIZdl7YieX9l2Nky5GG7SWemipu/ycmAv37i/FDRY4dA3r3Fr1mZs8GFi9GWJjI1/z0E1DrnT/FzBpljZOoKHHj7Q28+abY5vG6N4B48fJ4eGj21jFSkiShXq//wW72LIQ/KO5R5F/PH6ueXYX+/v1ljK7yZs8Wub3DhzVHrZH+JSYWJ3QA8ThpkvjvWtN67BQldYxlBiwmdfRMkiRcuXNFPaTq5TYvY3TgaPX6no16wsrCCoWqQthb2aOHbw91bZzmrs1r3JCqspw+Le4QhIWJmx2TJolZE3185I6MiIiIjN2hQ6LDRMnv5uXJys/CpL3FPZ1XDliJZq7N9Bidbk2fLnoyr14tRu/o2wD/Abjw5gUsPrYYi44tQoGyALdzbmN02Gisb7weq55dhRZuLXR3wF27RDftwkJRRKikTz4R3ZQA4I8/gG7dite5uop9AOD2bWRlAVOnitIzQ4cCWOYF3LolMmEFBZpTIY0YIWrYeHoCTZpoHtPBQZzsGkyhUOCHS98jz/KfhE6hHd4OmofFz70D21q28gZXSRx2Ja+4uOKEThGlUuRTa2pShz11arC7eXcRfj0cB64dwMFrB5GQnaBe52TrpJHUqWNdB58/+zmaujTF0z5Pw6aWecy3p1SK4cLLlwMnToj6cp99Jnqq1qkjc3BERERkMo4eLZ5I6ElmH5qN2zm3AQD9/fpjXNtxeoxM9zw8RJ3czz4TCR57A9Ssta1liwU9F2Bsm7F4e//b+DXuVwDA7zd+R5t1bbBvzD70aaJlN6n4eFG5OD5edK166y3N9e+9J74BOjqWTuqU/FZ4+7bmugYNRGKmQQOgfXt88AGQnQ18/vk/63/5RZyssrpuNGlSOpljZj4b8BkOXDuAPr7P4srnK7D3G1+83x2wdZM7Mu0VDbvq1YvDruQSECD+i5VM7Fhaiu95NU3J2a+MAZM6OvJnyp8IuxiGg9cPIup2FCRIZW5XNH67pDc6ms9fnuxsMdby88+BmzeBHj1EcmfwYNbLISIiospJTRU5gKeffvK2pxJPYUP0BgDiptr6wetNskf0rFmiTsWmTWImUEPxq+eHvS/uxZ7YPXh739u4lXULTe280bVhV80Nt28XY+nj44Gvvwaeeqp4XV5ecabF3r50UsfbW/yDZmeLH8cSw+K6dBHdb7y9RQKnJAcHUVgJYsbUVTOBJUtKTDjFO4YAgEJlIdaeWQtvR2883+J59fJGzo1w6a1LaOTcCDe7ihmxBg8GfvvNNCYnkSTRy5/DruTl7S3+Nk2aVFxiav36mtdLB2BPnRpBkkTCpmRDYP/V/fjv0dIFz2xr2aK7b3f0a9IP/fz6mdysAbpy65b4DN+4UXyejx4thlt16CB3ZERERGSqTpwQj9okdTp5dcKyfssw7/d5WNxnMXydffUbnJ40aQK88IKYEfSNNwArfU1IJElAeroY1vRP8WCFQoGhzYZi4DvrUXgkFbUU6bCaqTlEJzvhGhyLihHfvKmZ1Ck5tj4xsfQx331XJHq8vQE7O811Tz/9xH9opVJ8oWzTRuR/SJAkCb/E/YKZB2fiyp0raODQAP39+mtMYd/IuZF4bAT8+qvo8TJsmOjkZGvko7BWrwa++grYsYPDruQ2YYKooXP1quihUxMTOkDxnycmdUzMvfx7+P3G7zh47SAOXDuAsJFh6OBVnJHo59cPc8LnAABa12+Nfn4iidOtYTfYWdmV97I13smTYkKB778Xteneektk0ktOLEBERERUFcePizyBNnX4LC0sMSNkBka0GAEfJ9Mu3DdnjkhcfP01MK6qI8hUKjEV982bYtyEW4mxNteuAa1biztxL70kvi2XUCs3D7Xui5mxkJWlLkB8OeMyPjr/Mb4BoKpXFxb5+ZrHtLcXmbgGDcougqTtOLpyrFkDnDsnDlGL33IAiNEE7xx8B+E3wtXLbufcxv6r+zGi5Ygy9+nQAdizR/xzvPiiKHVkrOfz6FExFHHaNHGpkvy8vWtuMqeIlZX4YaFkI/dI9QhRt6NEgePrBxGZGKkxvd/Bawc1kjptPdpi+/Dt6N24N7wcvOQI2Wg8eiSmIF+xAjh1SrQTvvhCNDpq137y/kRERETaOH5cu146JZlqD52SWrcGhgwBFi8GXn65guEm6ekiQZOQAIwcqbluxQpg5kzx/Ouvxbf3Ip6eIqEDiO7WjwsIEMkgX19RzMTJCZIkYer+qTji/wi13wMs6hRibkAKphfmad7gDAmp6tuuUFKSKB49caIYqWXuErIS8GHEh9gSs0WjLMTTPk9jef/lCG4QXOH+PXqIZM7w4cBrrwGbNxvfsKbERCA0FOjaFfj0U7mjIXNjb8+eOkZr54Wd2HVxF8KvhyOrIKvMbawtrZGRm6GxzEJhgbFtxhoiRKOVlQV8+aUYZhUfL7pt7tkDDBxofB8CREREZNry8oCzZ4ExY8rfRpIk3M65DW/HmnfbeO5coNtTjxCx4i/0bnxDjAd49lnNjUaOBI4cEc+zsjRr1PiWSG7dvKm5n729+Kbs4AC0b1/64OvXl1okQULfJn1xMuEkch7mAA/vY97v87DuzDosemYRXmz9IiwU+msQzpghhgktWqS3Q5iEu3l3sejoInxx+gsUKAvUyxs7N8anfT/FiBYjtK4lNXgwsG0bMHasmDhsxQrAWMpQFRSIhI61NfDtt3ochkhUDiZ1jNivcb/ih0s/lFrewrUF+vn1Q3+//uju211jDKq5u35dJHI2bRJ/YF98UXSDbNdO7siIiIiopjpzRpR7qainzv6r+zF051C80eENfNDjA9SvXd9wAerKnTtAdLRocHXpoi4SHBIC9O2aj94z/0m6dO1aOqnTuHFxUufmTTFmq0iLFuJbe6NGQMeOpY9bVBdHSxYKC8x8aiZeafsKPoz4EOvProdKUiEhOwFjd4/F0pNLseiZRejv11/nBaoPHhRf7L/6CqhbV6cvbXI+ivgIn5/+XP27k40T5nWbhymdp1RpivKXXgLu3RMlFAoLRe97uW/WSpIoEh4TIy7T+ib435pMX+3aTOoYrX5+/bD9/HbUs6uHvk36op9fP/Rt0tfkx17rmiSJLs8rVojZq5ydRUG6t94qe4g0ERERkS4dPy4mNWrduuz1SpUS7/72Lh6pHmFV1Cp0bdgVowNHGzZIbV26BJw/LxI3U6dqzlX+22+iMjIAfPKJxsxP0z+og7T+bqiPdODGjdKv26+feC1fX8DVVXNdq1aiS7WO1a9dH2sGrcFbnd7CrEOzsO/qPgBATEoMnt3xLHr49sCukbvgVls382Xn54v2Z8+eokeJuZvTdQ6+PPcllColpgRPwZyuc+Bi71Kt13zzTdET5o03gLt3Re8da2sdBVxJkiTqaW/cKIaEdeokTxxE7KljxAY3HYyo16MQ5BEESwvOsf24wkIxa9WKFUBUFNC8uShK9/LLmu0PIiIiIn06flx0XCmvgOv289vxV9pfAMTMVyNbjSx7Q0OQJCAiQtS3kSTg9dc11//f/wH/+594PmQIEFhittQmTYqfX7+usVvfvsBGnymQVMDExX4o1f/lxRc1a+UYUKv6rfDrmF9x6NohzAmfg+jkaABAZn5mtZMMJX3yiSj7s2eP8QwNMoTcwlysO7MOdrXs8O9O/1Yv93TwxP+G/w8dvTrq9Kb0668D9eqJnjuZmWISFEPXypQk4J13xPeQlSuBV1817PGJSmJSx4g52zqjo1cZXVDNXGamyIh/8YUoStanj5jicMAA+btgEhERkXlRqcQMR1OmlL0+rzAPHxz+QP37p30/1Ws9FwBijMqJEyJxExgoigsWUSiAESNEg6phw9JJHT+/4ufXr2smdQICgNmzxTYdOmjsplAAjTd9gH79AJ+6wEDdv6tq6+vXF880eQZhF8Pw/u/v4/96/1+pf4uL6RfR0q1lpV87NlbU0Jk9W9xoNAd38+5iTdQafB75OdJz01HPrh7GthkLBxsH9TbDWwzXy7FHjAD27QOee674u0C9eno5VCmSJDqxffEFsGqV6J1FJCd7e85+RSbi6lWRCd+yRfTSeeklUS+n5JBsIiIiIkO6ckUMAymvns7nkZ8jMTsRADAoYBB6NuqpmwPHx4s5s69eFY2ikmPOL10CBg0Sz//9b82kDiCSMmfOiJmoCgoAG5vidf37i0LHjRsDwY/NSuTsLKa5KkefPqKczoIFoqSOMfZWsVBYYFSrURjefDhqWWh+/TiRcAJPb34a/fz6YfZTs9G7cW+tau5Ikvhi36CBmPWqpkvISsCKUyuw4ewGPCgs/iaZmZeJQ9cP4fkWzxskjt69gd9/F9dax45i4jR9zzamUgGTJwNr1wLr1gGTJun3eETaYE8dMmqSJIqOrVgB/PQT4OIiZhR4803Aw0Pu6IiIiMjcHT8uegqX9WXyTu4dLDompkCyUFjgkz6fVO7FL14UWaO0tNLfHjdsAD7+WDxv0UIzqePvX/z82rXSr/v220B2tkjuPJ60CAmp8lTfCgXw0UfAM88Ae/eK0VvGysqy9BRF//njPwCAg9cO4uC1g2jn0Q4zuszA6MDRsLYsv3DL//4nyg39+qvIh9VEkiThZOJJrDq9Crsu7sIj1SP1uqJE2bxu8xBYP7CCV9G9Tp2A06dFXrNrV2DhQlHnxlIPlSvu3hW1fMLCxCy7Eybo/hhEVVG7NpCR8eTtDIFJHZkkJgJxcaJHrbeRzLJZWAh8951I5pw9C7RsKWasHDu25n5YEhERkek5flz0GnZwKL3u46MfI6sgCwDwartXS3/hffRIJG1iYwEnJ9H1oKQxY8S0OrVqAf/6l+ZcySUTN1evau7n6iq6y/j6ag6fKvLyy9q/wUrq1Qvo0QOYP19MaGWMvXXKIkkSRrQYgSsZV3Djnij0HJMSg1d+fAVzw+firU5v4dWgV+FRR/OuYkaGuOH44oulJ/yqSYZ8MwS/xP2iscy2li3+1e5feOepd9CkbpNy9tS/Jk3ETeCPPgLef18k2LZvFz2ndOXAAfFfMDdXzG42UsayWESPM6aeOqyGIoNNm8Tnfe/e4nHTJnnjuXtXFJlr3FgkcFxcgP37gQsXxJBvQyV0EhOBw4fFIxEREVF5jh8ve+hV+oN0rDuzDlaPgDaZNlic11VU0S0pNVUkXZ5/Hli+vPSLFCVuHj0qvW+XLiJx87//le4So1AAH34oqrcaeEqeot46MTFiVlJToVAo8Fr71xA7JRY7R+xEJ6/i83Y75zbe+/09+KzwQeh3ofgr9S/1upkzxT/PihVyRK0fkiSVWlbyfLjau+L9bu/j1rRbWD1otawJnSJWVsB//wuEh4scaevWYqRgTk71XvfBAzG0bsAAMUnbX38xoUPGh0kdM5aYCEycKMaGAuJx0iR5EhmxseIPpo+PaIMMGCD+aB44IIZ2G/Iuj7EluoiIiMg4paWJ3s5dn1KJpMuhQ+qG1cbojch7lIdXY4A/VxbA5YVXxfickry8iqfsjIsrfYBRo8R4kq+/Lj0NePPmotE0Zoy4G2ZEevQQ7agPPyxuZ5qKWha1MDpwNCJfi8Qf4//AsObDoPhnLq9Hqkf4/tL3eKh8CEAkELZtA5YuBdzd5YxaN25k3sDiY4vRak0rxN3RvB4ndpiIEO8QbBu2DQnTE/Cf3v9B/dr1ZYq0fL16AX/+Kf7rzJ8v2vIffSTqgldGRoao5dmmjajnuXq1+F5iLKMaiEoypqQOh18ZWFxc6Q9apVL04DXEHyxJAo4cETem9u4VbZXZs0U9v/oyfUaUl+jq359/xImIiMyaJAHp6aJb8T/TG504IVYN3R4KjNktfomPB3x8ML3LdDjbOuPUnUUA/rlj9njiRqEQ9W1sbUVdnMeZcJeAjz4CunUT002b4ttQKBTo5tsN3Xy74UbmDXwZ/SU2x2yGl4MXOnh1QF6eqK/Sowdg0X4r/u9oEkJbhqKpS1O5Q6+UKxlX8P2l7/H9pe/VU70DwLY/t+G/vf+r/t3TwRMnJpyQI8RKc3ERRYzff18k3D75BFi2TCR6QkKAzp3Ff7fH6+4UFIjCy5s3i1qegJhd6+OPgaam9c9KZoazX5mxgABR2K9kYsfSUnOItj48fCjGoi5fLrrmtmolio299JJo08hJ7kQXERERySw3VzRWnJ2Ll6lUohBxWpoY13H+PAAx9MrHB7Bv1QTY/8+2sbGAjw/srOzwZqc38W/v4UDadNHwenwWKkDMg10Dde0K9OsnvlgPG6ZZDsjUNK7bGB8/8zE+7PkhbufcBiCG+sTHixuTI39fjr/S/sK83+fBr64fBgYMxMCAgejh2wN2VsZVDDKnIAdHbh3BwWsHcej6IVzOuFzmdtczrxs4Mt3z9gY++wyYO1c8/vKLSNhIElCnjuiFU1AA3Lkjeubcvy/2a90aWLJEdIJ7vIMckTFiTx0z5u0tJk6YNEkkLiwtRTFifSUv7twRWfPVq4HkZFFM7tNPxfSXxlJET65EFxERERmQUikeS96qj4oCRowQ03zPmaOZbLGwAOrVKx5vpVIBFhbF9XS6dBGZi4AAzVmoACg8PYGdO/X+lozRp58CQUFi+ue335Y7muqzsrRCI+dG+Osv8d7mzwes6l/HX2nFNXauZV7DF6e/wBenv4BtLVsENwjGU95P4emGT6O7b3c42jgaNGZJkjSmZZ91aBbWn11f5rbtPdtjRIsRGN1qNPzq+RkqRL1zdxf/nRctEjV2zpwBIiNFqYc6dUTixsVFPAYGimvWWL6bEGmjdm3jSeoopLKqchm57OxsODk5ISsrC46Ohv0jrSuJiaInir+/fhI6ly+L7PhXX4nM+CuvAFOnihmtjNGmTaUTXZyykIjI9NWEz+yaQrZ/i02bRKGMuDhR/6Zr1+J116+LKb4BUbj4++819500CbhxQ4zDWLwY+Za14eQkhnVMnly8mVKlhKWFHuZTNlGvvy5O5dWrIi9m6goLxfCdhw/FDK02NsDNezex6+9d+PXqrzgWf0xjuu+SIsZFoEejHurfE7MTkXI/BT6OPnCr7QYLRdVKjCpVSqQ+SEXsnVhcybiCK3fET0xKDI7/6zgaOTdSbxt2MQwjd4nxcJYKS3Tx7oLhzYfj+RbPo3Fd46rNRETa2bJFzM5WWCgmS9S1ynxms6eOTLy9dZ/MkSQxJnXFCtHV0d0deO890R5yc9PtsXRtwgRRQ0efiS4iIiLSkw8+EPMbp6QAly5p3nJ/8EDcngfEMKmSSR1fX8DDQzyWVd9mfXHvhsREYPdu8cX+qaeKN5EkCU9tfgqt3Fph5lMz0dLNSO9gGdB//iM6Ki1cKG7ymbr/+z8x+i4yUiR0AKCRcyPMenoWZj09C1n5Wfjt+m/Yd3Ufwm+E4+a9mwBEAeZODTRnIttxfgfmhM9Rr/eo44EGDg3gYu8Cu1p2sLeyh72VPUK8QzCu3TiNfQd9PQgJWQlIfZCKjNwMqKSyK1JHJkZqJHV6N+6NNzu+ib5+fdGrUS842Trp5sQQkWyK6u3n5QEODvLGwqRODVBQAHzzjUjmnD9fXDH+xReLP/iqIzFR3FwLCNBvskUfiS4iIiIygNOnxUwMgBguVXJaoubNRXEXP7/SVVItLcX48CfYtElzUoUzZ4D27cXzg9cO4vTt0zh9+zQupl/EqddO6eANmY6y2mkeHuLG3vz5YjKMZs3kjbE6oqNFLZ333gM6dCh7GydbJ4xoOQIjWo4AACTlJOFEwgncuncL9lb2GttezLiofv5I9QiJ2YlIzC49DW16bnqppE5MSgyScpIqjNfRxhF38+5qLKtnVw+rB62ucD8iMi1FSZ0HD5jUoWpITy+ul5OaCgwaJAoh9+6tuzGpJRtRFhaiHhCHRZExMFSykYiItNCsGXDwoJhKMzlZM6nTu7coPFDF/umPz5IJAG++CQwcKP7+f3riU/Xy2U/Pruo7MEkVtdOmTxcdnWbNAn7+Wd44q6qgABg3TtRcef997ffzcvBCaMvQMtf1a9IPNpY2SMpJUv+kPUiDBM2KFAWPCkrt61HHAxm5GXCv7Y76tevDo44H/Or6oZlrMzRzaYZmrs3QwKGBRj0dIqqZipI6xlBXhzV1TNDFi6Ir7fbtInnzyivAtGnqmT51JjFR9IZ+vIDxzZv8Ek3yYrKRyHSY+2e2MdHrv0VamuiNU7eubl8XwOHDIi9U1nKHpmfRcWNHAEBAvQBceuuS2dTW0aad9u23wAsvAL/9BjzzjCxhVst774npsc+cET3R9aVQWYichznILcxFXmEecgtzYVPLBs1dNRvXuYW5sKtlx6QNEeHkSTEU+K+/ROJZ1yrzmV21ymBkcJIkags++6yYjnzPHnHHIj5e9NbRdUIHqHiqcSK5PH7HVqUSdaMSS/ecJiIiQ6lfXy8JHaB4lsySimbJXHdmnXrZOyHvmE1CB9CunTZqFBASAsyYUTz5mKmIjAQWLwYWLNBvQgcQM2zVs6sHb0dvBLgEoK1H21IJHQCwt7JnQoeIAIjZrwDj6KnDpI6Ry88HNm8WH2b9+on6g9u2ibsw8+aJaQD1paJGFJFcmGwkIjIv3t6iR2ZRm8TCQgwrcnTLxjcXvgEAOFg7YGybsTJGaXjatNMUClFz8a+/xARkpuL+fTHsqn174N135Y6GiKg0Yxp+xaSOkUpLAz76SHSrfe01oHFj0c04OloMt9JFAeQnKWpEFdU0LJpqnEOvSE5MNhIRmZ8JE4A5cwBbWzEL+oQJwDd/fYMHhQ8AAGPbjEVt69oyR6kpMVG03fTVk1TbdlrnzsDbb4ubgbGx+olFlySpuAfu9u36mSrYnOn7uiQyF0zqULkuXBANlYYNgU8/BUJDgcuXRYG7nj11VwBZWxMmiF5Bhw+LR9YtIbkx2UhEZJ7i4oDgYHHDCwA2RG9Qr3u9/esyRVW2TZtEnL17i8dNm/RzHG3baf/3f0CDBsCrrxr/MKyNG4Gvvxaf9fooL2DODHVdEpmDkrNfyY1JHSMgScD+/WJ4VevWwIEDopdOQoKY2appU3nj8/YWCSV+aSZjwWQjEZH5OXMG6NRJPD+bdBbRydEAgI5eHRHkGSRjZJoMXftNm3aavT2wZYso7Pn55/qJQxdiYkSvojfeAF56Se5oahbWJCTSLfbUIQBAXp64G9GqlSiAfPcu8L//ATduiPHD9erJHSGR8WKykYjIfGRkiPZRx47//J6bAb+6fgCAie0nyhhZacZa+61bN5Ewee894xyGlZ0NjBwJtGwp6gCRbhnrdUlkqqysxIgBY0jqcJSqDFJSgDVrgLVrgTt3gOeeE8NHunY1/PAqIiIiImN35ox4LOqp09+/P2KnxOLwjcMIbhAsX2BlKKr99vhU48ZQ++3jj4G9e4F//Qs4cqR4KLPcJEnUkExLA/btE7WTSLeM+bokMkUKhZgByxiSOuypY0Dnz4uxzL6+wPLlwIsviqz57t3i7gkTOkRERESlnTkjZkxv0qR4mYXCAs80eQYONg7yBVYGY679Vru2GIZ14gTw2WdyR1Ns2TJg1y4x4yuTDPphzNclkamyt2dSxyyoVMAvvwB9+gBt2wK//Qb8979i/OrnnwN+fnJHSERERPp2//59LFiwAAMGDEC9evWgUCiwdetWrfe/d+8eJk6cCDc3N9SuXRu9evVCdHS0/gI2MlFRYuiVqdwAM+bab926ATNmiKH+v/0mdzSiKPKsWcDcucCIEXJHU7MZ83VJZIpMNqnDRol2cnOBdevEuODBg8U44W++EdNwzpoFODvLHSEREREZSkZGBhYuXIhLly6hbdu2ldpXpVJh0KBB+PrrrzF58mR8+umnSEtLQ8+ePREXF6eniI3LmTMiqXPz3k1czrgsdzhaMebab598Im44jhwJXLkiXxyHDgHjxwPjxomhYaR/xnxdEpkae3sTnf2KjZKKJSUB8+YBPj7AW28BgYHAsWNAZCTwwguioBIRERGZF09PTyQnJ+PWrVtYsmRJpfYNCwvDiRMnsHXrVixYsABvvfUWIiIiYGlpiQULFugpYuORlCR+OnUCFh9bjBarW6DH1h6Iu1Mz2o5yqFUL+PZbwNNT3Hy8c8fwMZw9Czz/PNC3r5g4xFR6YRERFTHZnjpslJTt3DnglVeARo3EsKqXXxbV5MPCgKef5gcVERGRObOxsYGHh0eV9g0LC4O7uzuef/559TI3NzeMGjUKP/30EwoKCnQVplEqKpLcOqgA31z4RixLOgP3Ou4yRmX6nJyAPXuAzEwgNBR4+NBwx752DRg4UPRo/+473vQkItNkskkdNkqKqVTiw7BXL6B9ezGLwCefiHo5n30GNG4sd4RERERk6s6dO4f27dvDwkKz2RYcHIzc3FzEljM/dUFBAbKzszV+TFFUFODuDlzI34+sgiwAwPMtnoejjaPMkZk+Pz8xYcfx46KHuSTp/5ixsaJ3jrOzqDtZu7b+j0lEpA9mOftVVRslxubBAzElefPmwNChQH6+6MJ67ZooPOfkJHeEREREVFMkJyfD09Oz1PKiZUlJSWXut2jRIjg5Oal/fHx89BqnvhTV09n5Ty8dAHgx8EUZI6pZunUTsyJ9+SXw5ptAYaH+jnXkCNClC2BjAxw8CLi66u9YRET6ZrI9daqjqo0SY7nTdPu2qMzv4wNMmQK0ayemhDx5Ehg1SoxPJiIiItKlvLw82NjYlFpua2urXl+WuXPnIisrS/2TkJCg1zj1QZJET522nR5gT+weAEA9u3ro26SvzJHVLOPHi6TOl1+KGjtZWbo/xrZtoodO+/ai7ezrq/tjEBEZkskWSq6OqjZK5L7TdPYsMHasqJezerX44Lt2TYwBDgkxaChERERkZuzs7Mocop6fn69eXxYbGxs4Ojpq/JiaW7dEEd/Cxj8jt1DcDg1tEQorSxZh0bUJE4ADB4DTp4GnngJu3NDN66pUwPvvF89ytW8fZ4E1d4mJYlr1xES5IyGqHrPsqVPVRokcd5qUSuCnn4AePUSX3+PHgU8/FX98li8XCR4iIiIifSuapOJxRcu8vLwMHZLBREWJxz9VJYZetebQK33p3Vv0oikoADp3Bv74o3qvFxUlak/+3/+JdvSGDSyKbO42bRK9tHr3Fo+bNskdEVHVmWVSp6qNEkPeabp/H1i1CmjWDBg2DHj0CNi1C4iLA6ZPB0zwJhcRERGZsHbt2iE6OhoqlUpjeWRkJOzt7dG0aVOZItO/M2cAb/9MHE7YDwDwcvBCt4bdZI6qZmveHDh1Sjz26AE8+6xI9FTGzZvAmDFAcDBw966onzNrFmeDNXeJicDEiaL3FiAeJ01ijx0yXWaZ1DH2Rkl0tKiXM22a6J1z6pTooRMayno5REREpH/Jycm4fPkyCktUqw0NDUVqaip++OEH9bKMjAzs2rULQ4YMKXNoe01x5gzg1uMHFKrE+RjVchQsLSxljqrmc3UVw2O++QaIjxfDsfr0ASIixA3Psty9C/z6KzB1qkgI/f67qNETEyP2JYqLK07oFFEqgatX5YmHqLqMZfYrvaUqkpOTkZWVBT8/P1j9088yNDQUYWFh+OGHHxAaGgrAuBolrVoBkycDr78ONGwoayhEZAISE0UDJSAA8PaWOxoiMnarVq3CvXv31BND7NmzB4n/3KKeMmUKnJycMHfuXGzbtg03btxAo3/GeoeGhqJLly549dVXcfHiRbi6umLNmjVQKpX46KOP5Ho7eidJwLlzwNCeDeHeZAAOXTvEoVcGZGkJvPCCmAxk925g4UIxlMrSUtwEbdxY/CiV4kbolStiv/r1gTlzgJkzgTp15H0PZFwCAgALC83EjqUl4O8vX0xE1WEsPXWqlNSpqY0SGxvgP/+ROwoiMgWbNhV3IbawEHUCJkyQOyoiMmZLly7FrVu31L//8MMP6t43Y8eOhZOTU5n7WVpa4tdff8WsWbPw+eefIy8vD506dcLWrVvRrFkzg8Quh4QEIDMTCA3qi8GD+yL9QTpc7TkHtqFZWAAjRgDDhwPHjgGXLgHXr4tCyufPi+TbM8+IYsghIUCTJhxmRWXz9hbtpUmTRDLQ0hJYv543xsh02dsD+fnF17NcFJIkSZXdqVGjRhqNkpKKkjjjx48vldQBgMzMTMyaNQs//vijulGydOlSdOzYUevjZ2dnw8nJCVlZWSY5kwMRmbbERFHc7/E7TTdvsmFC9Dh+ZhsPU/u3+Pln4LnnxN/cBg3kjoaIdCUxUQy58vdnu4lM2zffAC+9BOTk6L5nYmU+s6vUU+fmzZtP3Gbr1q3YunVrqeV169bFl19+iS+//LIqhyYikl1FY8LZOCEi0o1z50Rtlxo8uReRWfL2ZnuJagZ7e/GYmyvvcFODFkomIqoJisaEl8Qx4UREunXqQjJ8eh2AUiqnMi+RFhITRdFnzrBERLpWMqkjJyZ1iIgqqWhMeNHYWY4JJyLSvcjcb3Cu1QB4LfPC/qv75Q5H75h80L1Nm8Rw6d69xeOmTXJHREQ1Se3a4pFJHSIiEzRhgqihc/iweGSRZCIi3cnMBDLr/wwASM9NRyPnRvIGpGdMPuheYmLxhAaAeJw0iUkzItKdop46Dx7IGweTOkREVeTtDfTsyR46RES6diTqDtDwGACgqUtTNHdtLnNE+sPkg35UVP+OiEgXOPyKiIiIiKgMu87tAyyUAIChTYfKHI1+MfmgH6x/R0T6xqQOEREREVEZjmfsUT8f0myIjJHoH5MP+sH6d0Skb0zqEBERERE95qHyIRJs9gEA6tnVw1M+T8kckX4x+aA/rH9HRPpkLEmdWvIenoiIiIio2KHYI1BZ5QAABgUMQi2Lmt9cnTAB6N9fDLny92dCR5e8vXk+iUg/bGyATz8FgoLkjaPmf0oS1RCJiWLcfUAAGydERFRzfXX6Z/Xzoc1qdj2dkph8ICIyLQoFMGuW3FFw+BWRSeBUp0REZA4kSUJ4gqinY21pjf5+/WWOiIiIyLgxqUNk5DjVKRERmYu8R3nwvD8QtXK90bNRTzjYOMgdEhERkVFjUod0LjFRFKRj0kE3ONUpERGZC3srezgdW4ORifHYOWKn3OEQEREZPSZ1SKc4TEj3ONUpERGZC5UK+PNPIKidAnXt6sodDhERkdFjUod0hsOE9INTnRIRkbm4dg24f1/+mUSIiIhMBWe/Ip2paJgQExDVw6lOiYiopou9E4sjUc4A6qNdO5mDISIiMhFM6pDOFA0TKpnY4TAh3eFUp0REVJO9c/Ad7I39Bdb/7gLJ7icAbnKHREREZPQ4/Ip0hsOEiIiIqCryH+Uj/Ho4oJCgqHsTLvYucodERERkEthTh3SKw4SIiIioso7HH0feozwAQLNa/WGh4H1HIiIibTCpQzrHYUJERERUGb9d/039vLdvXxkjISIiMi28DUJEREREsjp0/ZD6+Yud+8gYCRERkWlhUoeIiIiIZHMn9w6ik6MBABZpbdGxRX2ZIyIiIjIdTOoQERERkWzCb4RDggQA8MjtCwu2TomIiLTGj00iIiIiks2ha8VDr4IcOfSKiIioMpjUISIiIiJZSJJUXE/nkTX6Nusmb0BEREQmhkkdIiIiIpJFzsMceDl4wVJhCcR3Rad29nKHREREZFKY1CEiIiIiWTjaOOLEhBNY7XsH2LsOgYFyR0RERGRaaskdABERERGZt6sXnNDYyQmOjnJHQkREZFrYU4eIiIiIZHX+PNCmjdxREBERmR4mdYiIiIjI4B4qH0KSxFTmf/7JpE5NkJgIHD4sHomIyDCY1CEiIiIig/v0+KfwWeGD0d+MR2rhVbRtK3dEVB2bNgG+vkDv3uJx0ya5IyIiMg9M6hARERGRwR26fgi3c27ju9htgKoWe+qYsMREYOJEQKUSv6tUwKRJ7LFDRGQITOoQERERkUHdf3gfJxNOAgBcFf6wf9gITZrIHBRVWVxccUKniFIJXL0qTzxEROaEs18RERERkUH9cesPFKoKAQD17vVFk0DA0lLmoKjKAgIACwvNxI6lJeDvL19MRETmgj11iIiIiMigfr/xu/r5w8t9WE/HxHl7Axs2FCfmLC2B9evFciIi0i/21CEiIiIigzoaf1T9/PaJ7mjzXxmDIZ2YMAHo318MufL3Z0KHiMhQmNQhIiIiIoN58PABopOjAQB+ji1xLcuVRZJrCG9vJnOIiAyNw6+IiIiIyGBOJZ7CI9UjAIAvugEAWreWMyIiIiLTxaQOERERERlMyaFX9hnd4OMD1K0rY0BEREQmjEkdIiIiIjKYyNuR6uf3/+7GIslERETVwKQOERERkZ4VFBTg3XffhZeXF+zs7NC5c2ccOnToift9+OGHUCgUpX5sbW0NELV+/PzCz4h8LRKrB65G3JmGrKdDRERUDSyUTERERKRn48ePR1hYGKZNm4aAgABs3boVAwcOxOHDh9G1a9cn7r927VrUqVNH/btl0dzRJsjK0grBDYLhZxuMt26DSR0iIqJqYFKHiIiISI9Onz6NnTt3YsmSJZg5cyYA4JVXXkFgYCBmz56NEydOPPE1QkND4erqqu9QDer8efHIpA4REVHVcfgVERERkR6FhYXB0tISEydOVC+ztbXFhAkTcPLkSSQkJDzxNSRJQnZ2NiRJ0meoBnX+PGBrCwQEyB0JERGR6WJSh4iIiEiPzp07h6ZNm8LR0VFjeXBwMAAgJibmia/RpEkTODk5wcHBAWPHjkVqauoT9ykoKEB2drbGj5xUkgqjw0bj0+Of4mzSWZw/D7RqBdRiv3EiIqIq48coERERkR4lJyfD09Oz1PKiZUlJSeXuW7duXUyePBkhISGwsbHB0aNHsXr1apw+fRpnzpwplSgqadGiRfjoo4+q/wZ05ELaBXz393f47u/vMLz5cMT/+QNat5Y7KiIiItPGpA4RERGRHuXl5cHGxqbU8qIZrPLy8srdd+rUqRq/jxgxAsHBwRgzZgzWrFmDOXPmlLvv3LlzMWPGDPXv2dnZ8PHxqWz4OnP01lH186d9umHf38DYsbKFQ0REVCNw+BURERGRHtnZ2aGgoKDU8vz8fPX6ynjppZfg4eGB3377rcLtbGxs4OjoqPEjp6PxxUmdRoruyM8He+oQERFVE5M6RERERHrk6emJ5OTkUsuLlnl5eVX6NX18fHD37t1qx2YokiThj1t/AADqWNfBo8S2ADjzFRERUXUxqUNERESkR+3atUNsbGypQsWRkZHq9ZUhSRJu3rwJNzc3XYWod9czryP5vkhiPeXzFP7+qxbc3QETegtERERGiUkdIiIiIj0KDQ2FUqnEhg0b1MsKCgqwZcsWdO7cWV3nJj4+HpcvX9bYNz09vdTrrV27Funp6RgwYIB+A9ehkkOvujXshvPn2UuHiIhIF1gomYiIiEiPOnfujJEjR2Lu3LlIS0uDv78/tm3bhps3b2LTpk3q7V555RUcOXIEkiSpl/n6+mL06NFo3bo1bG1tcezYMezcuRPt2rXDpEmT5Hg7VVI09AoQSZ0tfwHDh8sYEBERUQ3BpA4RERGRnn311Vf44IMPsH37dmRmZqJNmzbYu3cvunfvXuF+Y8aMwYkTJ/D9998jPz8fvr6+mD17NubNmwd7e3sDRV99RT11rC2t0cIxGNevs0gyERGRLiikkreDTER2djacnJyQlZUl+0wOREREVD5+ZhsPuf4t0h+ko/7S+gBEPZ1lLY8jJASIjgaCggwWBhERkcmozGc2e+oQlSExEYiLAwICAG9vuaMhIiIyXVaWVlg7aC0ib0ci0C0Q588DlpZAixZyR0ZERGT6mNQhesymTcDEiYBKBVhYABs2ABMmyB0VERGRaXK2dcYbHd/AGx3fAABM+QZo2hSwtZU5MCIiohqAs18RlZCYWJzQAcTjpEliOREREVXf+fOsp0NERKQrTOoQlRAXV5zQKaJUAlevyhMPERFRTSJJ4HTmREREOsSkDlEJAQFiyFVJlpaAv7888RAREZmytAdp+O36b8jKzwIA3L4N3LvHpA4REZGuMKlDVIK3t6ihY2kpfre0BNavZ7FkIiKiqtgXtw99t/dF3cV1sTZqLc6fF8s5/IqIiEg3WCiZ6DETJgD9+4shV/7+TOgQERFVVeTtSACABAlNXZrizO+AgwPg6ytzYERERDUEkzpEZfD2ZjKHiIiouoqSOgoo0KlBJ2z+p0iyQiFzYERERDUEh18RERERkc7lFebhfKoYb9XCrQUcbRxZJJmIiEjHmNQhIiIiIp07l3IOj1SPAACdG3TGw4fA5ctM6hAREekSkzpEREREpHORiZHq58ENgnH5MvDoEYskExER6RJr6hARUaUplUoUFhbKHQYZCWtra1hY8D4RaSqqpwOInjp//S6eM6lDRESkO0zqEBGR1iRJQkpKCu7duyd3KGRELCws0LhxY1hbW8sdChmRoqSOXS07tHZvjZ3ngYYNAScnmQMjIiKqQZjUISIirRUldOrXrw97e3soOIWN2VOpVEhKSkJycjIaNmzIa4IAAGkP0nDz3k0AQAevDqhlUQt//cV6OkRERLrGpA4REWlFqVSqEzouLi5yh0NGxM3NDUlJSXj06BGsrKzkDoeMQMr9FLSu3xp/p/+Nzg06AwDOnQN69wYSEwFvb5kDJCIiqiGY1CEiIq0U1dCxt7eXORIyNkXDrpRKJZM6BABo494G5/99HjkFOShQFuDzz4GUFODrr4GdO4ENG4AJE+SOkoiIyPSxqiEREVUKh9fQ43hNUHkcbByQf9cV06cXL1OpgEmTRI8dIiIiqh4mdYiIiIhIb+LiRCKnJKUSuHpVnniIiIhqEiZ1iIiIiEhnJEnS+D0gAHi8M5elJeDvb8CgiIiIaqhKJ3UKCgrw7rvvwsvLC3Z2dujcuTMOHTr0xP0+/PBDKBSKUj+2trZVCpyIiEgXbt68CYVCgZiYGK332bp1K5ydnfUWE5Ep235+O5p+0RQv734ZZ5POwttbTGVelNixtATWr2exZCIiIl2odKHk8ePHIywsDNOmTUNAQAC2bt2KgQMH4vDhw+jatesT91+7di3q1Kmj/t3S0rKyIRAREVE5du3ahQ8++AA3b95EQEAAFi9ejIEDB8odFpmR07dPI+5uHOLuxmF82/FQKoHUVOCDD4BevUQPHSZ0iIiIdKNSSZ3Tp09j586dWLJkCWbOnAkAeOWVVxAYGIjZs2fjxIkTT3yN0NBQuLq6Vi1aIiIiKteJEyfw4osvYtGiRRg8eDC+/vprDBs2DNHR0QgMDJQ7PDIT51LOqZ+392yPuDggPx/o0QPo2VO+uIiIiGqiSg2/CgsLg6WlJSZOnKheZmtriwkTJuDkyZNISEh44mtIkoTs7OxS462JiIj0Yf/+/ejatSucnZ3h4uKCwYMH49q1a+VuHxERAYVCgV9++QVt2rSBra0tunTpggsXLpTa9sCBA2jRogXq1KmDAQMGIDk5Wb0uKioKffv2haurK5ycnNCjRw9ER0fr5T0WWblyJQYMGIBZs2ahRYsW+M9//oP27dtj1apVej0uURGlSomYlBgAQGPnxqhrVxfnz4t1bdrIFxcREVFNVameOufOnUPTpk3h6OiosTw4OBgAEBMTAx8fnwpfo0mTJrh//z5q166NYcOGYdmyZXB3d69k2EREZAxyc4HLlw1/3ObNAXt77bZ98OABZsyYgTZt2uD+/fuYP38+hg8fjpiYGFhYlH9vY9asWVi5ciU8PDzw3nvvYciQIYiNjYWVlRUAIDc3F0uXLsX27dthYWGBsWPHYubMmdixYwcAICcnB+PGjcMXX3wBSZKwbNkyDBw4EHFxcXBwcCjzmDt27MCkSZMqfD/79u1Dt27dylx38uRJzJgxQ2NZ//798eOPP1b4mkS6Enc3DrmFuQBELx0A+PNPwMsLYEdtIiIi3atUUic5ORmenp6llhctS0pKKnffunXrYvLkyQgJCYGNjQ2OHj2K1atX4/Tp0zhz5kypRFFJBQUFKCgoUP+enZ1dmbCJiEhPLl8GOnQw/HHPngXat9du2xEjRmj8vnnzZri5ueHixYsVDklasGAB+vbtCwDYtm0bvL29sXv3bowaNQoAUFhYiHXr1sHPzw8AMHnyZCxcuFC9f+/evTVeb8OGDXB2dsaRI0cwePDgMo85dOhQdO7cucL306BBg3LXpaSklLpR4u7ujpSUlApfk0hXopOLe6MFeQQBEEmdtm3lioiIiKhmq1RSJy8vDzY2NqWWF81glZeXV+6+U6dO1fh9xIgRCA4OxpgxY7BmzRrMmTOn3H0XLVqEjz76qDKhEhGRATRvLhIschxXW3FxcZg/fz4iIyORkZEBlUoFAIiPj68wqRMSEqJ+Xq9ePTRr1gyXLl1SL7O3t1cndABxgyMtLU39e2pqKt5//31EREQgLS0NSqUSubm5iI+PL/eYDg4O5fbiITIF55I16+kAwPnzwEsvyRURERFRzVappI6dnZ1Gj5ki+fn56vWV8dJLL+Gdd97Bb7/9VmFSZ+7cuRrdybOzs584zIuIiPTP3l77HjNyGTJkCHx9fbFx40Z4eXlBpVIhMDAQDx8+rNbrFg3DKqJQKDTqxY0bNw537tzBypUr4evrCxsbG4SEhFR43OoOv/Lw8EBqaqrGstTUVHh4eDzp7RDpRHRKiZ46nkG4exdISGBPHSIiIn2pVFLH09MTt2/fLrW8qDCkl5dXpQPw8fHB3bt3K9zGxsamzB5CREREFblz5w6uXLmCjRs3qhMhx44d02rfU6dOoWHDhgCAzMxMxMbGokWLFlof+/jx41izZo16OvGEhARkZGRUuE91h1+FhIQgPDwc06ZNUy87dOiQRq8jIn2RJEndU8ezjic86njgyBGxjkWSiYiI9KNSSZ127drh8OHDyM7O1qiBExkZqV5fGZIk4ebNmwgKCqrUfkRERNqoW7cuXFxcsGHDBnh6eiI+Pr7CnqElLVy4EC4uLnB3d8e8efPg6uqKYcOGaX3sgIAAbN++HR07dkR2djZmzZr1xB6t1R1+NXXqVPTo0QPLli3DoEGDsHPnTpw5cwYbNmyo8msSaetW1i1k5mcCEL10AFFPx8YGaNZMzsiIiIhqrkpNaR4aGgqlUqnROCwoKMCWLVvQuXNn9ZCo+Ph4XH5sOpT09PRSr7d27Vqkp6djwIABVYmdiIioQhYWFti5cyfOnj2LwMBATJ8+HUuWLNFq308++QRTp05Fhw4dkJKSgj179sDa2lrrY2/atAmZmZlo3749Xn75Zbz99tuoX79+Vd+KVp566il8/fXX2LBhA9q2bYuwsDD8+OOPFdYOItIVjzoeiBgXgeX9luNf7f4FQNTTadUKqFWp24hERESkLYVUsgCAFkaNGoXdu3dj+vTp8Pf3x7Zt23D69GmEh4eje/fuAICePXviyJEjGrUF7O3tMXr0aLRu3Rq2trY4duwYdu7cibZt2+L48eOw13ZuWoiaOk5OTsjKyqpw1iwiItKd/Px83LhxA40bN1YXyK+JIiIi0KtXL2RmZsLZ2VnucExCRdcGP7ONhxz/Fp06AYGBwJYtBjkcERFRjVCZz+xK3zf56quv8MEHH2D79u3IzMxEmzZtsHfvXnVCpzxjxozBiRMn8P333yM/Px++vr6YPXs25s2bV6mEDhEREREZv0ePgAsXgDFj5I6EiIio5qp0UsfW1hZLliypsPt6REREqWUbN26s7KGIiIiIyERdvQrk57NIMhERkT5xhDMREVEJPXv2RCVHJhOZvTu5d/DNhW8Q5BGEth5tUce6Dv78U6zjdOZERET6U6lCyUREREREjzt9+zSm7JuCrlu6YsHhBQBEkeQGDQAXF5mDIyIiqsGY1CEiIiKiajmXck79vL1newBiOnMOvSIiItIvJnWIiIiIqFqik6PVz4M8gwCInjocekVERKRfTOoQERERUbUU9dSxq2WHZi7NcPcukJDAnjpERET6xqQOEREREVXZvfx7uJ55HQDQ1qMtLC0scf68WMeeOkRERPrFpA4RERERVVlMSoz6eXsPUU/n/HnAxgZo2lSmoIiIiMwEkzpERGTWbt68CYVCgZiYGK332bp1K5ydnfUWE9U8BQUFePfdd+Hl5QU7Ozt07twZhw4d0mrf27dvY9SoUXB2doajoyOee+45XL9+Xc8Ra69kPZ2SRZJbtQJq1ZIrKiIiIvPApA4REVEN8ffff2PEiBFo1KgRFAoFPvvsszK3W716NRo1agRbW1t07twZp0+fNmygZmj8+PFYvnw5xowZg5UrV8LS0hIDBw7EsWPHKtzv/v376NWrF44cOYL33nsPH330Ec6dO4cePXrgzp07Boq+YiVnvipZJJn1dIiIiPSPSR0iIqIaIjc3F02aNMEnn3wCDw+PMrf59ttvMWPGDCxYsADR0dFo27Yt+vfvj7S0NANHaz5Onz6NnTt3YtGiRViyZAkmTpyI33//Hb6+vpg9e3aF+65ZswZxcXHYu3cvZs+ejenTp+PgwYNITk7GsmXLDPQOKnYuWSR1rCys0MqtFQoLgb/+AoKCZA6MiIjIDDCpQ0RENdr+/fvRtWtXODs7w8XFBYMHD8a1a9fK3T4iIgIKhQK//PIL2rRpA1tbW3Tp0gUXLlwote2BAwfQokUL1KlTBwMGDEBycrJ6XVRUFPr27QtXV1c4OTmhR48eiI6OLvUautSpUycsWbIEL7zwAmxsbMrcZvny5Xj99dfx6quvomXLlli3bh3s7e2xefNmvcZmzsLCwmBpaYmJEyeql9na2mLChAk4efIkEhISKty3U6dO6NSpk3pZ8+bN8cwzz+C7777Ta9zakCQJbdzbwL+eP1q7t4ZNLRtcugQUFDCpQ0REZAgc6UxERNWy/ORyLD+5/Inbtfdsj59f/Flj2dBvhmrU4yjPjJAZmBEyo0rxPXjwADNmzECbNm1w//59zJ8/H8OHD0dMTAwsLMq/tzFr1iysXLkSHh4eeO+99zBkyBDExsbCysoKgOgVs3TpUmzfvh0WFhYYO3YsZs6ciR07dgAAcnJyMG7cOHzxxReQJAnLli3DwIEDERcXBwcHhzKPuWPHDkyaNKnC97Nv3z5069atSufi4cOHOHv2LObOnateZmFhgT59+uDkyZNVek16snPnzqFp06ZwdHTUWB4cHAwAiImJgY+PT6n9VCoVzp8/j3/961+l1gUHB+PgwYPIyckp93oqKChAQUGB+vfs7OzqvI0yKRQKfD3iawDAQ+VDAMC5f0ZjtWun88MRERHRY5jUISKiaskuyMbtnNtP3M7HqfSX1vTcdK32zS6o+pfRESNGaPy+efNmuLm54eLFiwgMDCx3vwULFqBv374AgG3btsHb2xu7d+/GqFGjAACFhYVYt24d/Pz8AACTJ0/GwoUL1fv37t1b4/U2bNgAZ2dnHDlyBIMHDy7zmEOHDkXnzp0rfD8NGjSocH1FMjIyoFQq4e7urrHc3d0dly9frvLrUsWSk5Ph6elZannRsqSkpDL3u3v3LgoKCp64b7Nmzcrcf9GiRfjoo4+qGnalWVtaAxBJnYAAoJxcExEREekQkzpERFQtjjaOaODw5ESDm71bmcu02dfRxvGJ25QnLi4O8+fPR2RkJDIyMqBSqQAA8fHxFSZ1QkJC1M/r1auHZs2a4dKlS+pl9vb26oQOIL5kl6xLk5qaivfffx8RERFIS0uDUqlEbm4u4uPjyz2mg4NDub0uyHTl5eWVORzO1tZWvb68/QBUaV8AmDt3LmbMKO7hlp2dXWaPIF2LjubQKyIiIkNhUoeIiKqlOkOjHh+OpQ9DhgyBr68vNm7cCC8vL6hUKgQGBuLhw4fVet2iYVhFFAoFJElS/z5u3DjcuXMHK1euhK+vL2xsbBASElLhcfU9/MrV1RWWlpZITU3VWJ6amlpuYWWqPjs7O41hUEXy8/PV68vbD0CV9gVEMqi82kr6olIBMTHAoEEGPSwREZHZYlKHiIhqrDt37uDKlSvYuHGjOhHypCmki5w6dQoNGzYEAGRmZiI2NhYtWrTQ+tjHjx/HmjVrMHDgQABAQkICMjIyKtxH38OvrK2t0aFDB4SHh2PYsGEARN2W8PBwTJ48ucqvSxXz9PTE7dulhxkWFdb28vIqc7969erBxsZGowC3tvvK5fp1ICeHPXWIiIgMhUkdIiKqserWrQsXFxds2LABnp6eiI+Px5w5c7Tad+HChXBxcYG7uzvmzZsHV1dXdSJEGwEBAdi+fTs6duyI7OxszJo1q8JeFUD1h189fPgQFy9eVD+/ffs2YmJiUKdOHfj7+wMAZsyYgXHjxqFjx44IDg7GZ599hgcPHuDVV1+t8nGpYu3atcPhw4eRnZ2tUSw5MjJSvb4sFhYWaN26Nc6cOVNqXWRkJJo0aWJ0w/WKJnhjUoeIiMgwOKU5ERHVWBYWFti5cyfOnj2LwMBATJ8+HUuWLNFq308++QRTp05Fhw4dkJKSgj179sDa2lrrY2/atAmZmZlo3749Xn75Zbz99tuoX79+Vd+KVpKSkhAUFISgoCAkJydj6dKlCAoKwmuvvabeZvTo0Vi6dCnmz5+Pdu3aISYmBvv37y9VPJl0JzQ0FEqlEhs2bFAvKygowJYtW9C5c2d1nZv4+PhSBatDQ0MRFRWlkdi5cuUKfv/9d4wcOdIwb6ASzp0DvL0Bt9IltIiIiEgPFFLJAgAmIjs7G05OTsjKyio1PSgREelHfn4+bty4gcaNG6uLtNZEERER6NWrFzIzM+Hs7Cx3OCahomuDn9nCqFGjsHv3bkyfPh3+/v7Ytm0bTp8+jfDwcHTv3h0A0LNnTxw5ckSjNlNOTg6CgoKQk5ODmTNnwsrKCsuXL4dSqURMTAzcKpE9McS/Rf/+gI0N8LP+y2URERHVWJX5zObwKyIiIiI9++qrr/DBBx9g+/btyMzMRJs2bbB37151Qqc8Dg4OiIiIwPTp0/Hf//4XKpUKPXv2xIoVKyqV0DEESRI9df79b7kjISIiMh9M6hARERHpma2tLZYsWVLh8L+IiIgyl3t7e2PXrl16ikx3kpKA9HSgfXu5IyEiIjIfTOoQERGV0LNnT5jgyGQi2bFIMhERkeGxUDIRERERVdu5c4CLC/BP3WciIiIyACZ1iIiIiKjazp0TvXQUCrkjISIiMh9M6hARERFRtUVHc+gVERGRoTGpQ0RERETVcucOEB/PIslERESGxqQOEREREVVLTIx4ZE8dIiIiw2JSh4iIiIiqJToaqF0bCAiQOxIiIiLzwqQOERGZtZs3b0KhUCCmqKuBFrZu3QpnZ2e9xURkas6dA9q1AyzYsiQiIjIofvRWQWIicPiweCQiIjIWf//9N0aMGIFGjRpBoVDgs88+K3O71atXo1GjRrC1tUXnzp1x+vRpjfX5+fl466234OLigjp16mDEiBFITU01wDsgU8UiyURERPJgUqeSNm0CfH2B3r3F46ZNckdEREQk5ObmokmTJvjkk0/g4eFR5jbffvstZsyYgQULFiA6Ohpt27ZF//79kZaWpt5m+vTp2LNnD3bt2oUjR44gKSkJzz//vKHeBpmY+/eB2FgmdYiIiOTApE4lJCYCEycCKpX4XaUCJk1ijx0iImO2f/9+dO3aFc7OznBxccHgwYNx7dq1crePiIiAQqHAL7/8gjZt2sDW1hZdunTBhQsXSm174MABtGjRAnXq1MGAAQOQnJysXhcVFYW+ffvC1dUVTk5O6NGjB6Kjo/XyHot06tQJS5YswQsvvAAbG5syt1m+fDlef/11vPrqq2jZsiXWrVsHe3t7bN68GQCQlZWFTZs2Yfny5ejduzc6dOiALVu24MSJEzh16pRe4yfTdP48IEmc+YqIiEgOTOpUQlxccUKniFIJXL0qTzxEREZh+XLA21v8RERorrtxo3jdlCml9x06tHj947ZuLV73ww9VDu/BgweYMWMGzpw5g/DwcFhYWGD48OFQPf4H/TGzZs3CsmXLEBUVBTc3NwwZMgSFhYXq9bm5uVi6dCm2b9+OP/74A/Hx8Zg5c6Z6fU5ODsaNG4djx47h1KlTCAgIwMCBA5GTk1PuMXfs2IE6depU+HP06NEqn4uHDx/i7Nmz6NOnj3qZhYUF+vTpg5MnTwIAzp49i8LCQo1tmjdvjoYNG6q3ISrpxg3A3h5o2VLuSIiIiMxPLbkDMCUBAaIAYMnvAZaWgL+/fDEREckuOxu4fVs8LyjQXKdUFq/LzCy9b3p68frHPXhQvC43t8rhjRgxQuP3zZs3w83NDRcvXkRgYGC5+y1YsAB9+/YFAGzbtg3e3t7YvXs3Ro0aBQAoLCzEunXr4OfnBwCYPHkyFi5cqN6/d+/eGq+3YcMGODs748iRIxg8eHCZxxw6dCg6d+5c4ftp0KBBhesrkpGRAaVSCXd3d43l7u7uuHz5MgAgJSUF1tbWpQpBu7u7IyUlpcrHppprzBhg5EjA2lruSIiIiMwPkzqV4O0NbNgghlwplSKhs3592TeYiYjMhqMjUJRoeHzIj6Vl8bq6dUvv6+ZWvP5xtWsXr7O3r3J4cXFxmD9/PiIjI5GRkaHuoRMfH19hUickJET9vF69emjWrBkuXbqkXmZvb69O6ACAp6enRl2a1NRUvP/++4iIiEBaWhqUSiVyc3MRHx9f7jEdHBzg4OBQpfdJJCcmdIiIiOTBpE4lTZgA9O8vhlz5+zOhQ0SEGTPET1kaN6648NjPP5e/bvx48VNNQ4YMga+vLzZu3AgvLy+oVCoEBgbi4cOH1XpdKysrjd8VCgUkSVL/Pm7cONy5cwcrV66Er68vbGxsEBISUuFxd+zYgUmTJlV43H379qFbt25VitnV1RWWlpalZrJKTU1VF1b28PDAw4cPce/ePY3eOiW3ISIiIiLjwKROFZRX/oGIiIzLnTt3cOXKFWzcuFGdCDl27JhW+546dQoNGzYEAGRmZiI2NhYtWrTQ+tjHjx/HmjVrMHDgQABAQkICMjIyKtxH38OvrK2t0aFDB4SHh2PYsGEAAJVKhfDwcEyePBkA0KFDB1hZWSE8PFw9dO3KlSuIj4/X6L1ERERERPJjUoeIiGqsunXrwsXFBRs2bICnpyfi4+MxZ84crfZduHAhXFxc4O7ujnnz5sHV1VWdCNFGQEAAtm/fjo4dOyI7OxuzZs2CnZ1dhftUd/jVw4cPcfHiRfXz27dvIyYmBnXq1IH/PwXgZsyYgXHjxqFjx44IDg7GZ599hgcPHuDVV18FADg5OWHChAmYMWMG6tWrB0dHR0yZMgUhISHo0qVLlWMjIiIiIt3j7FdERFRjWVhYYOfOnTh79iwCAwMxffp0LFmyRKt9P/nkE0ydOhUdOnRASkoK9uzZA+tKFA7ZtGkTMjMz0b59e7z88st4++23Ub9+/aq+Fa0kJSUhKCgIQUFBSE5OxtKlSxEUFITXXntNvc3o0aOxdOlSzJ8/H+3atUNMTAz279+vUTx5xYoVGDx4MEaMGIHu3bvDw8MDP1RjBjIiIiIi0g+FVLIAgInIzs6Gk5MTsrKy4OjoKHc4RERmIT8/Hzdu3EDjxo1ha2srdzh6ExERgV69eiEzM7PUDFBUtoquDX5mGw/+WxAREZmGynxms6cOEREREREREZEJYlKHiIiIiIiIiMgEsVAyERFRCT179oQJjkwmIiIiIjPEnjpERERERERERCaISR0iIqoUlUoldwhkZNiziYiIiEgeHH5FRERasba2hoWFBZKSkuDm5gZra2soFAq5wyKZSZKE9PR0KBQKWFlZyR0OERERkVlhUoeIiLRiYWGBxo0bIzk5GUlJSXKHQ0ZEoVDA29sblpaWcodCREREZFaY1CEiIq1ZW1ujYcOGePToEZRKpdzhkJGwsrJiQoeIiIhIBkzqEBFRpRQNs+FQGyIiIiIiebFQMhERERERERGRCWJSh4iIiIiIiIjIBDGpQ0RERERERERkgkyypo4kSQCA7OxsmSMhIiKiihR9Vhd9dpN82H4iIiIyDZVpP5lkUicnJwcA4OPjI3MkREREpI2cnBw4OTnJHYZZY/uJiIjItGjTflJIJnjrTKVSISkpCQ4ODlAoFDp97ezsbPj4+CAhIQGOjo46fe2ahOdJezxX2uO50g7Pk/Z4rrSnr3MlSRJycnLg5eUFCwuO+paTvtpP/H+mPZ4r7fFcaY/nSjs8T9rjudKeMbSfTLKnjoWFBby9vfV6DEdHR17AWuB50h7PlfZ4rrTD86Q9nivt6eNcsYeOcdB3+4n/z7THc6U9nivt8Vxph+dJezxX2pOz/cRbZkREREREREREJohJHSIiIiIiIiIiE8SkzmNsbGywYMEC2NjYyB2KUeN50h7PlfZ4rrTD86Q9nivt8VxRVfHa0R7PlfZ4rrTHc6Udnift8VxpzxjOlUkWSiYiIiIiIiIiMnfsqUNEREREREREZIKY1CEiIiIiIiIiMkFM6hARERERERERmSAmdYiIiIiIiIiITBCTOkREREREREREJshskjoFBQV499134eXlBTs7O3Tu3BmHDh3Sat/bt29j1KhRcHZ2hqOjI5577jlcv35dzxHLo6rn6cMPP4RCoSj1Y2tra4Co5XH//n0sWLAAAwYMQL169aBQKLB161at97937x4mTpwINzc31K5dG7169UJ0dLT+ApZJdc7T1q1by7yuFAoFUlJS9Bu4gUVFRWHy5Mlo1aoVateujYYNG2LUqFGIjY3Van9zuZ6A6p0rc7qmAODvv//GyJEj0aRJE9jb28PV1RXdu3fHnj17tNrfnK4rKo1tJ+2x/aQdtp20x/aTdth+0h7bT9oztfZTLb29spEZP348wsLCMG3aNAQEBGDr1q0YOHAgDh8+jK5du5a73/3799GrVy9kZWXhvffeg5WVFVasWIEePXogJiYGLi4uBnwX+lfV81Rk7dq1qFOnjvp3S0tLfYYrq4yMDCxcuBANGzZE27ZtERERofW+KpUKgwYNwp9//olZs2bB1dUVa9asQc+ePXH27FkEBAToL3ADq855KrJw4UI0btxYY5mzs7NuAjQSixcvxvHjxzFy5Ei0adMGKSkpWLVqFdq3b49Tp04hMDCw3H3N6XoCqneuipjDNQUAt27dQk5ODsaNGwcvLy/k5ubi+++/x9ChQ7F+/XpMnDix3H3N7bqi0th20h7bT9ph20l7bD9ph+0n7bH9pD2Taz9JZiAyMlICIC1ZskS9LC8vT/Lz85NCQkIq3Hfx4sUSAOn06dPqZZcuXZIsLS2luXPn6i1mOVTnPC1YsEACIKWnp+s7TKORn58vJScnS5IkSVFRURIAacuWLVrt++2330oApF27dqmXpaWlSc7OztKLL76oj3BlU53ztGXLFgmAFBUVpccIjcPx48elgoICjWWxsbGSjY2NNGbMmAr3NafrSZKqd67M6Zoqz6NHj6S2bdtKzZo1q3A7c7uuSBPbTtpj+0l7bDtpj+0n7bD9pD22n6rHmNtPZjH8KiwsDJaWlhoZNVtbW0yYMAEnT55EQkJChft26tQJnTp1Ui9r3rw5nnnmGXz33Xd6jdvQqnOeikiShOzsbEiSpM9QjYKNjQ08PDyqtG9YWBjc3d3x/PPPq5e5ublh1KhR+Omnn1BQUKCrMGVXnfNUUk5ODpRKpQ4iMk5PPfUUrK2tNZYFBASgVatWuHTpUoX7mtP1BFTvXJVU06+p8lhaWsLHxwf37t2rcDtzu65IE9tO2mP7SXtsO2mP7SftsP2kPbafqseY209mkdQ5d+4cmjZtCkdHR43lwcHBAICYmJgy91OpVDh//jw6duxYal1wcDCuXbuGnJwcnccrl6qep5KaNGkCJycnODg4YOzYsUhNTdVHqCbv3LlzaN++PSwsNP8LBgcHIzc3V+txwOaiV69ecHR0hL29PYYOHYq4uDi5QzIISZKQmpoKV1fXCrfj9aT9uSpibtfUgwcPkJGRgWvXrmHFihXYt28fnnnmmQr34XVl3th20h7bT4bBv0mVZ26fdUXYftIe208VM5X2k1kkdZKTk+Hp6VlqedGypKSkMve7e/cuCgoKqrSvKarqeQKAunXrYvLkyVi/fj3CwsLw2muv4dtvv0W3bt2QnZ2tt5hNVXXOtTmxt7fH+PHjsXr1auzevRuzZ89GeHg4nnrqKa3ufJq6HTt24Pbt2xg9enSF2/F60v5cmes19c4778DNzQ3+/v6YOXMmhg8fjlWrVlW4D68r88a2k/bYfjIM/k3Snrl+1hVh+0l7bD9VzFTaT2ZRKDkvLw82NjallhfNLJCXl1fufgCqtK8pqup5AoCpU6dq/D5ixAgEBwdjzJgxWLNmDebMmaPbYE1cdc61ORk1ahRGjRql/n3YsGHo378/unfvjo8//hjr1q2TMTr9unz5Mt566y2EhIRg3LhxFW5r7tdTZc6VuV5T06ZNQ2hoKJKSkvDdd99BqVTi4cOHFe5j7teVuWPbSXtsPxkG/yZpz1w/6wC2nyqD7acnM5X2k1n01LGzsytz7Fp+fr56fXn7AajSvqaoquepPC+99BI8PDzw22+/6SS+mkTX59qcdO3aFZ07d67R11VKSgoGDRoEJycnda2Gipjz9VTZc1UWc7immjdvjj59+uCVV17B3r17cf/+fQwZMqTC+h3mfF0R206VwfaTYfBvUvWYw2cd20/aY/tJO6bSfjKLpI6npyeSk5NLLS9a5uXlVeZ+9erVg42NTZX2NUVVPU8V8fHxwd27d6sdW02jj3NtTmrydZWVlYVnn30W9+7dw/79+7W6Fsz1eqrKuSpPTb6myhIaGoqoqKgKx3Wb63VFAttO2mP7yTD4N6n6avJ1xfaT9th+qjpjbT+ZRVKnXbt2iI2NLTU2OTIyUr2+LBYWFmjdujXOnDlTal1kZCSaNGkCBwcHnccrl6qep/JIkoSbN2/Czc1NVyHWGO3atUN0dDRUKpXG8sjISNjb26Np06YyRWYarl+/XiOvq/z8fAwZMgSxsbHYu3cvWrZsqdV+5ng9VfVclaemXlPlKer6m5WVVe425nhdUTG2nbTH9pNh8G9S9dXUzzq2n7TH9lP1GGv7ySySOqGhoVAqldiwYYN6WUFBAbZs2YLOnTvDx8cHABAfH4/Lly+X2jcqKkqjcXLlyhX8/vvvGDlypGHegIFU5zylp6eXer21a9ciPT0dAwYM0G/gRi45ORmXL19GYWGhelloaChSU1Pxww8/qJdlZGRg165dGDJkSJnjMGu6ss5TWdfVr7/+irNnz9a460qpVGL06NE4efIkdu3ahZCQkDK34/VUvXNlTtcUAKSlpZVaVlhYiK+++gp2dnbqxhyvK3oc207aY/tJ9/g3SXvm/lnH9pP22H7Snqm1nxRSRQPCapBRo0Zh9+7dmD59Ovz9/bFt2zacPn0a4eHh6N69OwCgZ8+eOHLkiMYYuZycHAQFBSEnJwczZ86ElZUVli9fDqVSiZiYmBqXmazqebK3t8fo0aPRunVr2Nra4tixY9i5cyfatm2L48ePw97eXq63pFerVq3CvXv3kJSUhLVr1+L5559HUFAQAGDKlClwcnLC+PHjsW3bNty4cQONGjUCIP6odu3aFRcuXMCsWbPg6uqKNWvWID4+HlFRUWjWrJmM70r3qnqeAgICEBQUhI4dO8LJyQnR0dHYvHkzPD09ERUVBXd3dxnflW5NmzYNK1euxJAhQzQK0RUZO3YsAPB6QvXOlTldUwAwfPhwZGdno3v37mjQoAFSUlKwY8cOXL58GcuWLcOMGTMA8LqisrHtpD22n7THtpP22H56MraftMf2k/ZMrv0kmYm8vDxp5syZkoeHh2RjYyN16tRJ2r9/v8Y2PXr0kMo6JQkJCVJoaKjk6Ogo1alTRxo8eLAUFxdnqNANqqrn6bXXXpNatmwpOTg4SFZWVpK/v7/07rvvStnZ2YYM3+B8fX0lAGX+3LhxQ5IkSRo3bpzG70Xu3r0rTZgwQXJxcZHs7e2lHj16SFFRUYZ/EwZQ1fM0b948qV27dpKTk5NkZWUlNWzYUPr3v/8tpaSkyPNG9Kjo/1V5P0V4PVXvXJnTNSVJkvTNN99Iffr0kdzd3aVatWpJdevWlfr06SP99NNPGtvxuqKysO2kPbaftMe2k/bYfnoytp+0x/aT9kyt/WQ2PXWIiIiIiIiIiGoSs6ipQ0RERERERERU0zCpQ0RERERERERkgpjUISIiIiIiIiIyQUzqEBERERERERGZICZ1iIiIiIiIiIhMEJM6REREREREREQmiEkdIiIiIiIiIiITxKQOEREREREREZEJYlKHiIiIiIiIiMgEMalDRERERERERGSCmNQhIiIiIiIiIjJBTOoQEREREREREZmg/weqjVi125si6gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge #上述这种正则化叫岭回归，sklearn中在Ridge方法中。\n",
    "#见：https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge\n",
    "np.random.seed(42)\n",
    "m = 20 #样本个数20\n",
    "X = 3*np.random.rand(m,1)#X在0到3之间\n",
    "y = 0.5 * X +np.random.randn(m,1)/1.5 +1 #np.random.randn(m,1)是在0.5*x+1的基础上出现抖动，np.random.randn(m,1)/1.5为了使抖动小一点。\n",
    "X_new = np.linspace(0,3,100).reshape(100,1) #为了绘图，设置的测试数据。\n",
    "\n",
    "def plot_model(model_calss,polynomial,alphas,**model_kargs): #model_calss:model,polynomial:是否用多项式，alphas:岭回归权重，model_kargs：设置成**,即收集剩余的关键字参数\n",
    "    for alpha,style in zip(alphas,('b-','g--','r:')): #zip(a,b)函数分别从a和b依次各取出一个元素组成元组，再将依次组成的元组组合成一个新的迭代器--新的zip类型数据。\n",
    "        # 比如alphas为一个元组(0,10,100)，则zip把0和'b-'分配为一组，10和'g--'分配为一组，100和'r:'分配为一组。\n",
    "        model = model_calss(alpha,**model_kargs) #model_calss在后面实例化时输入为Ridge，即model_calss就是Ridge，然后这里调用岭回归方法\n",
    "        if polynomial: #如果有采用多项式，则用多项式回归\n",
    "            model = Pipeline([('poly_features',PolynomialFeatures(degree =10,include_bias = False)),\n",
    "             ('StandardScaler',StandardScaler()),\n",
    "             ('lin_reg',model)])#即这里把上述定义的岭回归模型传入('lin_reg',model)参数中，即采用多项式回归+岭回归正则化，之后覆盖model作为传出的返回值model\n",
    "        model.fit(X,y)\n",
    "        y_new_regul = model.predict(X_new)\n",
    "        lw = 2 if alpha > 0 else 1 #指定线条宽度，当alpha > 0 时线条宽度为2，当alpha > 0 不成立时线条宽度为1.\n",
    "        plt.plot(X_new,y_new_regul,style,linewidth = lw,label = 'alpha = {}'.format(alpha))\n",
    "    plt.plot(X,y,'b.',linewidth =3)\n",
    "    plt.legend()\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.subplot(121)\n",
    "plot_model(Ridge,polynomial=False,alphas = (0,10,100))\n",
    "plt.subplot(122)\n",
    "plot_model(Ridge,polynomial=True,alphas = (0,10**-5,1))#10**-5是10^{-5}\n",
    "plt.show() #可以看到alpha越大，拟合的曲线越平稳，越不易过拟合。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "惩罚力度越大，alpha值越大的时候，得到的决策方程越平稳。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "![title](./img4/线性回归/10.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "#见：https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html#sklearn.linear_model.Lasso\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.subplot(121)\n",
    "plot_model(Lasso,polynomial=False,alphas = (0,0.1,1))\n",
    "plt.subplot(122)\n",
    "plot_model(Lasso,polynomial=True,alphas = (0,10**-1,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "多做实验，得出结果！！！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "思考题："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1如何利用梯度下降来实现线性回归？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.初始化参数：随机初始化直线的斜率和截距。\n",
    "2.计算损失函数：利用当前的参数计算损失函数的值，通常使用平方损失函数。\n",
    "3.计算梯度：计算损失函数关于参数的梯度，即损失函数对斜率和截距的偏导数。\n",
    "4.更新参数：根据梯度的方向和学习率（步长），更新参数。参数的更新规则通常是沿着梯度的反方向移动一小步，以减小损失函数的值。\n",
    "5.重复迭代：重复步骤 2-4，直到损失函数收敛或达到预定的迭代次数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2什么是批量梯度下降？随机梯度下降？小批量梯度下降？它们的区别是什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "批量梯度下降：使用整个训练集来计算梯度，并更新参数\n",
    "特点是：迭代速度很快，但是成本很高，要耗费的算力最多\n",
    "\n",
    "随机梯度下降：随机梯度下降每次只使用单个样本来计算梯度，并更新参数。\n",
    "特点是计算成本低，但是样本可能不具有足够的代表性，进而影响参数稳定\n",
    "\n",
    "小批量梯度下降：每次使用一小批量来计算梯度，并更新参数。\n",
    "特点是计算成本低且更新方向比较稳定，但是需要一个额外的超参数。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3在线性回归中，梯度下降算法中的学习率是什么？学习率的取值过大或过小对算法的收敛有什么影响？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "学习率是指每次迭代更新参数时所乘以梯度的比例，学习率太大或者太小都会对收敛产生不好的影响。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4在数据生成中，如何在y = 1 + 7*X上生成50个随机抖动的数据，使得所有这50个标签不在一条直线上。\n",
    "其中，将X的取值范围设定在[0,10)之间，生成y的过程中采用高斯分布生成随机数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "X = 10 * np.random.rand(50)\n",
    "y = 1 + 7 * X + np.random.randn(50)\n",
    "for i in range(5):\n",
    "    print(\"X =\", X[i], \", y =\", y[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5给定方阵X，方阵Y，向量z，在满足矩阵和向量乘法的条件下，利用numpy如何计算（X乘以Y的转置）的逆再乘以向量z。即计算(X*Y^T)^{-1}*z."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X = np.array([[1, 2], [3, 4]])\n",
    "Y = np.array([[5, 6], [7, 8]])\n",
    "z = np.array([1, 2])\n",
    "XY_transpose = np.dot(X, Y.T)\n",
    "inv_XY_transpose = np.linalg.inv(XY_transpose)\n",
    "result = np.dot(inv_XY_transpose, z)\n",
    "print(\"结果：\", result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "6 如下代码\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])\n",
    "y = np.dot(X, np.array([2, 6])) + 1\n",
    "\n",
    "问上述代码得到的y是什么？上述代码中np.array的作用是什么？\n",
    "如何以X为特征，y为标签，用LinearRegression训练模型怎么用代码实现？\n",
    "R2统计量作为评估标准的得分怎么用代码实现？计算线性回归的系数和偏置项怎么用代码实现？\n",
    "给定新的特征[4, 1]下，做预测怎么用代码实现？\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、y是样本的标签值\n",
    "2、创建一个numpy数组,[2,6]应该是斜率\n",
    "3、代码如下\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "r2_score = model.score(X, y)\n",
    "coefficients = model.coef_  \n",
    "intercept = model.intercept_  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4、y = np.dot(X, np.array([2, 6])) + 1 对每个预测值加上常数项1，得到最终的标签值 y\n",
    "5、代码如下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_features = np.array([[4, 1]])\n",
    "predicted_y = model.predict(new_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7代码x2 = x1[m:n]中x2包含x1中的哪些元素？ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "包含索引为m到n-1的元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "8分析下述代码每句话的含义\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_features = PolynomialFeatures(degree = 4,include_bias = False)\n",
    "X_poly = poly_features.fit_transform(X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "第一行：导入了 scikit-learn 库中的 PolynomialFeatures 类\n",
    "第二行：创建了一个 PolynomialFeatures 对象，最多生成四次幂，不生成偏置项\n",
    "第三行：将原始特征矩阵 X 转换为多项式特征矩阵\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9在sklearn中要对数据进行标准化，需要import的代码是什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 给定特征集合X，标签y。把数据分割成训练集和测试集，其中训练集占75%，测试集占25%。代码如何实现？\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "11分析下述代码做了什么？  \n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "import numpy as np\n",
    "n_samples, n_features = 10, 5\n",
    "rng = np.random.RandomState(0)\n",
    "y = rng.randn(n_samples)\n",
    "X = rng.randn(n_samples, n_features)\n",
    "clf = Ridge(alpha=1.0)\n",
    "clf.fit(X, y)\n",
    "clf.predict(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "导入 Ridge 回归模型。\n",
    "\n",
    "import numpy as np：导入 NumPy 库，用于生成随机数据。\n",
    "\n",
    "n_samples, n_features = 10, 5：指定数据集的样本数量为 10，特征数量为 5。\n",
    "\n",
    "rng = np.random.RandomState(0)：使用随机种子为 0 创建一个随机状态实例 rng，确保代码的可重复性。\n",
    "\n",
    "y = rng.randn(n_samples)：使用随机状态生成一个包含 n_samples 个样本的随机标签 y，符合标准正态分布。\n",
    "\n",
    "X = rng.randn(n_samples, n_features)：使用随机状态生成一个包含 n_samples 行和 n_features 列的随机特征矩阵 X，符合标准正态分布。\n",
    "\n",
    "clf = Ridge(alpha=1.0)：创建一个 Ridge 回归模型的实例 clf，指定正则化参数 alpha 为 1.0。\n",
    "\n",
    "clf.fit(X, y)：使用训练集 X 和标签 y 对 Ridge 回归模型进行训练，拟合出最优的回归系数。\n",
    "\n",
    "clf.predict(X, y)：尝试调用 predict 方法对训练集 X 进行预测时，应该只传入特征矩阵 X 而不是标签 y，因此这行代码会报错。正确的调用方式是 clf.predict(X)，用训练好的模型对特征矩阵 X 进行预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12如何载入Lasso回归方法？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "13 下述代码的含义\n",
    "\n",
    "from scipy.io import loadmat\n",
    "mnist = loadmat('./mnist-original.mat')'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "导入 loadmat 函数，该函数用于加载 MATLAB 格式的数据文件。\n",
    "调用 loadmat 函数加载名为 mnist-original.mat 的 MATLAB 数据文件，并将加载的数据存储在变量 mnist 中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "14 如何在python中载入文件名stock_data.csv的csv格式的数据？进而载入列名为Close的收盘价数据，进行归一化，再分割训练集和测试集，占比8:2。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "导入 loadmat 函数，该函数用于加载 MATLAB 格式的数据文件。\n",
    "\n",
    "mnist = loadmat('./mnist-original.mat')：调用 loadmat 函数加载名为 mnist-original.mat 的 MATLAB 数据文件，并将加载的数据存储在变量 mnist 中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "15如何查看ndarray格式下X的数据规模？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用shape属性\n",
    "print(X.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "16 下述代码的目的是什么？\n",
    "\n",
    "import numpy as np\n",
    "shuffle_index = np.random.permutation(60000)\n",
    "X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "对训练集数据进行随机重排，以打乱训练样本的顺序，提高泛化能力"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "17如何将多重list表示的矩阵，拉直成一重？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import numpy as np\n",
    "matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n",
    "flat_matrix = np.array(matrix).flatten()\n",
    "print(flat_matrix)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "18随机梯度下降分类器如何调用？如何实例化,训练模型，并预测？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "##见19题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "19随机梯度下降回归器如何调用？如何实例化,训练模型，并预测？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###使用 SGDRegressor 类来实现随机梯度下降回归器\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "sgd_regressor = SGDRegressor(max_iter=1000, tol=1e-3, random_state=42)\n",
    "sgd_regressor.fit(X_train, y_train)\n",
    "y_pred = sgd_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "20以下哪种技术对于减少数据集的维度会更好 \n",
    "A.删除缺少值太多的列\n",
    "B.删除数据差异较大的列\n",
    "C.删除不同数据趋势的列\n",
    "D.都不是"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择：A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "21如果你训练的模型代价函数J随着迭代次数的增加，绘制出来的图如下，那么 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](.\\img4\\线性回归/tu21.png)\n",
    "#（显示错误，好吧，相对引用是这样的）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数呈现波动下降的趋势，所以说明损失值在逐渐降低，代价函数在逐渐减小，应该会收敛直到得到最优解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.无论你在使用mini-batch还是批量梯度下降，看上去都是合理的\n",
    "B.如果你正在使用mini-batch梯度下降，那可能有问题；而如果你在使用批量梯度下降，那是合理的\n",
    "C.如果你正在使用mini-batch梯度下降，那看上去是合理的；而如果你在使用批量梯度下降，那可能有问题\n",
    "D.无论你在使用mini-batch还是批量梯度下降，都可能有问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "22回归问题和分类问题的区别是？\n",
    "A.回归问题有标签，分类问题没有\n",
    "B.回归问题输出值是离散的，分类问题输出值是连续的\n",
    "C.回归问题输出值是连续的，分类问题输出值是离散的\n",
    "D.回归问题与分类问题在输入属性值上要求不同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "选择：C\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "23向量 x=[1,2,3,4,-9,0]的 L1 范数是多少？\n",
    "A.1\n",
    "B.19\n",
    "C.6\n",
    "D.15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择：B\n",
    "取绝对值并且相加，得19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "24下列哪种方法可以用来缓解过拟合的产生：\n",
    "A.增加更多的特征\n",
    "B.正则化\n",
    "C.增加模型的复杂度\n",
    "D.以上都是\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择：B\n",
    "过拟合：训练数据很好但是测试数据不好\n",
    "添加惩罚项，惩罚复杂度，降低过拟合风险"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "25在凸优化中只有一个全局最优解?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正确，凸优化函数的目标函数是凸函数，并且约束集是凸集，所以目标函数的局部最小值和全局最小值相等。而非凸优化的全局最优解不确定，就有可能存在多个局部最优解了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "26大部分的机器学习工程中，数据搜集、数据清洗、特征工程这三个步骤绝大部分时间，而数据建模，占总时间比较少？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正确，数据建模需要对于数据进行收集、清洗、转换原始数据并且提取有用信息的工作，这些工作会影响到数据建模的效率和水平，而在拥有比较高质量的数据和特征后，训练模型相对而言就比较简单了（虽然感觉也不是那么简单）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "27随机梯度下降，每次迭代时候，使用一个样本。\n",
    "A.正确\n",
    "B.错误"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择：A\n",
    "（BGD则使用整个训练集）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "28L2 正则化得到的解更加稀疏。\n",
    "A.正确\n",
    "B.错误"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择：A "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "29对训练数据归一化的根本原因是？\n",
    "A.归一化也可以说是一种正则化处理，可以提升模型泛化能力\n",
    "B.让模型更快的收敛\n",
    "C.加快参数初始化过程\n",
    "D.更容易对数据进行可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择：B\n",
    "（更快收敛防止梯度消失）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "30对数据进行标准化、归一化的代码分别如何实现？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设我们有'X_train'变量作为数据集中的特征数据\n",
    "标准化，即将数据特征值变为均值为0，标准差为1的正态分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "归一化，也就是将数据特征值缩放到一个范围内"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  }
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